Laurence Viale

Dorsaf Zouari

Impact of digitalization on procurement: the case of robotic process automation

Keywords: Digitalisation in procurement, RPA, Operation management, Case studies Digitalization in procurement, RPA, Operation management, Case studies. https://mc04.manuscriptcentral.com/scfij

A profound digital transformation is now underway. As the implementation of digitalization in supply chain processes has accelerated rapidly, coupled with an ever-increasing level of competitiveness, traditional procurement is changing to adapt to this new paradigm.

Research on RPA (Robotic Process Automation) in buyers' practices is still in its infancy.

The aim of the paper is thus to explore the practical implications and impact of RPA in the procurement function in order to improve our conceptual understanding of this recently introduced practice.

As first movers, seven case studies from various industries were selected based on their significant successes and experiences in the field of digitalization in the procurement sector.

The findings confirm the relevance of some recognized attributes of procurement automation, while other attributes have also been explored. The study suggests that RPA impacts on procurement in operational, organizational and relational terms. Furthermore, we identify the motives and challenges when implementing this type of initiative in the field of procurement.

According to [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF], digitalization has a real influence on organizations. In the context of strategic procurement initiatives, it presents new challenges and opportunities. Several authors have noted that digitization can play an important role, not only in aligning existing sourcing strategies but also in designing new sourcing strategies to achieve internal organizational growth and competitive advantage. Moreover, the procurement department acts as an interface between internal and external partners, and between inner capacities and external accomplices [START_REF] Van Weele | Value creation and purchasing strategy[END_REF]2014) and (Kosmol et al., 2019).

Since information technology tools play a key leverage role in purchasing and procurement (Kache and Seuring, 2017), the aim of this paper is to broaden the field in research on digital procurement and to confirm previous findings on the topic.

While many advanced technology tools can be used to improve the procurement process, some of them, such as Robotic Process Automation (RPA), can be particularly useful in gaining a competitive advantage [START_REF] Büyüközkan | Digital Supply Chain: Literature review and a proposed framework for future research[END_REF]. RPA technology not only gives these organizations an opportunity to improve their processes, but also helps them to simplify and rapidly streamline the end-to-end process even for those driven with high rules (Lacity et al., 2015).

The purpose of the present paper is to provide a better understanding of the current issues in procurement from a technological point of view and to examine the suitability of a technology such as RPA in the procurement process. We identify the tasks in which RPA can be used and highlight the benefits and drawbacks that may result. The findings are also intended to assist and guide companies thinking of adopting this technology in the future.

The research problem consists of one main question followed by three sub-questions. To what extent can robotic automation of processes be used to improve the purchasing process?

• What attributes can help avoid failure in procurement automation?

• How does RPA contribute to traditional procurement activities?

• What motives and challenges are inherent to the implementation of RPA? We begin by identifying the tasks for which RPA can be deployed in procurement in order to gain insights into the overall process and related tasks. By comparing these tasks with RPA success factors found in the literature, potential new uses can be found.

Secondly, we discuss RPA-related benefits. Investigating its benefits is important as it can explain not only how, but also why RPA should be used. RPA as a tool for process improvement is relatively new and therefore needs more validation and visibility in terms of its functionality.

We also investigate the difficulties and challenges related to RPA. This section seeks to find explain not only why RPA should be used, but also why it should not be used and the problems and risks companies may face. When businesses look for new tools and solutions to improve their current process, it is always crucial to examine the potential risks as well as the benefits of the chosen solution, especially when dealing with novel solutions with no prior knowledge, such as RPA.

Literature review 1.Theoretical background for Purchasing and Supply Management literature

While it is true that research in the field of Purchasing and Supply Management (PSM) is increasing and growing importance and is gaining significant academic interest [START_REF] Harland | Supply management: is it a discipline?[END_REF], [START_REF] Bentahar | Key success factors for implementing purchasing groups in the healthcare sector[END_REF], the field suffer from a lack of theoretical grounding robustness (e.g. [START_REF] Chicksand | Theoretical perspectives in purchasing and supply chain management: an analysis of the literature[END_REF][START_REF] Spina | Assessing the use of external grand theories in purchasing and supply management research[END_REF]. Since this research deals with the integration of an advanced technology, here RPA, in the procurement function, we therefore place procurement and digital procurement in their theoretical context.

According to Barney (2012) purchasing, and supply chain management be sources of sustained competitive advantage for a firm. Indeed, resource based view (RBV) contribute de better understand the make or buy decision by distinguishing between critical activities and non-critical and their contribution to have competitive advantage. In this way, critical activities could be maintained in-house, while non-critical activities are outsourced to external supplier. From the perspective of the RBV, the supplier is therefore seen as a potential source of a scarce resource (Barney, 2012) and [START_REF] Van Weele | The future of purchasing and supply management research: About relevance and rigor[END_REF]. In addition, it is well known that Procurement is the discipline that is concerned with the management of external resources [START_REF] Van Weele | The future of purchasing and supply management research: About relevance and rigor[END_REF].

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From the perspective of digitalization of purchasing and supply management [START_REF] Srai | Developing design principles for the digitalisation of purchasing and supply management[END_REF] have proposed a framework for research on digital procurement. The framework proposes a grid with multiple dimensions for digital technologies include those that directly impact purchasing processes and changes to procurement items.

Based on these two theoretical backgrounds, we are building a framework that permits us to show what are the drivers to adopt RPA as an advanced digital technology in order to improve the procurement process.

Motives

-Individual -Organisational Digital procurement RPA Procurement process improvement -Individual -Organisational Figure 1. Research framework 1.2.

Moving to digital procurement

The emergence of new technologies alters traditional ways of working and creates disruption across Supply Chain processes. In recent years, digitalization in the Supply Chain has attracted considerable attention from academic authors [START_REF] Benzidia | E-Design: Toward a New Collaborative Exchange of Upstream E-Supply Chain[END_REF], [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF], and management practitioners. Indeed, the "Digital Supply Chain" (DSC) is considered as one of the hottest buzzwords in the industry [START_REF] Büyüközkan | Digital Supply Chain: Literature review and a proposed framework for future research[END_REF].

The technological disruption that has been observed of late affects not only the production process (Smart Manufacturing/industry 4.0) and logistics (Logistics 4.0), but all other functional areas in a company, especially procurement. The aim is to make procurement processes leaner, but also to take the opportunities offered by process automation into consideration.

Global supply chains have deployed procurement to engage with the global supply base, and the resulting globalization is the main source of digitalization.

The adoption of new technologies presumes that a company is ready "to embrace and use new technological assets" [START_REF] Richey | Firm technological readiness and complementarity: capabilities impacting logistics service competency and performance[END_REF]. The digitalization of procurement requires an information technology system adapted to digital procurement, and human resources with the

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skills and knowledge required [START_REF] Benzidia | E-Design: Toward a New Collaborative Exchange of Upstream E-Supply Chain[END_REF] who are ready to get embedded in this kind of project [START_REF] Venkatesh | Adoption and impacts of interorganizational business process standards: role of partnering synergy[END_REF].

The information technology (IT) infrastructure available to organizations is one of the most important issues in digital procurement. This differs according to the organization due to various issues like their level of IT transformation and the company's size (Kache and Seuring, 2017).

Electronic data interchange (EDI) is an IT process that permits helps to build a platform for digital procurement practices by allowing information transfer within and across the firm's boundaries (Liu et al., 2016;Saldanha et al., 2015).

Integrated systems, such as the ERP system and data warehousing system, provide the "nerve system" for digital procurement by connecting separate systems on shared platforms [START_REF] Dong | Information technology in supply chains: the value of IT-enabled resources under competition[END_REF]. Some types of IT infrastructure used in digital procurement like the source-to-pay process are widely used in centralized IT and ERP systems.

Mature technologies also include cloud computing, which is a virtualized IT resource and can take the form of software as a service (SaaS), infrastructure as a service (IaaS), and/or platform as a service (PaaS) [START_REF] Wu | Cloud computing in support of supply chain information system infrastructure: understanding when to go to the cloud[END_REF].

Emerging technologies, like Artificial Intelligence, Machine Learning, Chatbots, RPA and Blockchain are only used in a small number of procurement processes, mainly in large companies. However, as procurement advances as a strategic function [START_REF] Chick | The Procurement Value Proposition: The Rise of Supply Management[END_REF], it is essential to get the correct and appropriate information from the huge amount of data available to support complex decision-making processes [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF].

Deloitte and Odgers Berndston (2018) identified key challenges to the effective application of digital technology in procurement. In their global study, the authors showed that the main drawbacks are a reduction in stakeholder endorsement and prioritization, as well as difficulties in getting the right skills/capability of analytics resources.

RPA to improve procurement processes

Robotic Process Automation (RPA) is defined as "a preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management" , 2017). RPA is not intended to disrupt existing information systems, but replaces a manual process with an automated one (Huang et al., 2019).

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According to a case study conducted in an energy services company (Lacity and Willcocks, 2015) before the rollout of RPA, an organization is unsuited for automation if the following attributes are not fulfilled:

• Unambiguous rules: as robots require precise instructions,

• Limited exception handling: exceptions mean the process is not worth automating and is better handled by humans.

• High and predictable volumes: no need to be stable, but predictable to control the average volume and performance

• Operating in a stable environment: otherwise the robot needs to be reconfigured

• Access to multiple systems: IT systems are faster than humans when moving from one system to another, so it reduces labor costs and time spent on the process.

• Known costs: evaluating the cost by considering the standard unit of "time/average" handling time it takes a human to achieve the process and resource costs (salary, wages, overheads, etc.)

In order to move forward with the digital agenda of tools such as RPA, AI and Blockchain, By applying these rules, it is possible to avoid failure in the rollout of RPA in procurement centers. However, another key reason for failure in the implementation of RPA is that firms tend to overestimate the potential gains, and lack the organization and skills needed to implement the tool [START_REF] Rutaganda | Avoiding pitfalls and unlocking real business value with RPA[END_REF].

RPA is increasingly used in procurement organizations [START_REF] Axcias | Purchasing, Supply Chain Management and Sustained Competitive Advantage: The Relevance of Resource-based Theory[END_REF]. Taking buyers away from low value-added and often time-consuming tasks should enable them to be more focused and efficient in more complex operations, spending more time working with internal customers and suppliers. In concrete terms, thanks to RPA, buyers will be able to delegate order receipts to a software robot, or configure their software robot to place automatic orders based on stock levels, for instance.

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Invoicing processes and supplier management are time-consuming activities for buyers.

According to Wax Digital study (2019) 1 , corporate financial controllers and buyers spend about 25% of their time trying to solve billing problems. Buyers are especially involved in this dispute resolution process as they are the first point of contact for suppliers.

In many organizations, tasks with low added value are outsourced. In a context of business strategies adopted to improve customer service, RPA helps organizations to save costs by no longer having to outsource (Lacity and Willcocks, 2015;Lacity et al., 2017). A study conducted by PwC ( 2016) 2 estimated that 45% of business activities can potentially be automated, resulting in a potential saving of $2 trillion of global labor costs. For buyers, this means that additional savings can be gained by using RPA instead of outsourcing activities.

In addition to saving buyers' time and company money, RPA minimizes human error as, if a robot is properly configured, it will not be inattentive, tired or subject to other errors [START_REF] Asatiani | Turning robotic process automation into commercial success-Case OpusCapita[END_REF][START_REF] Alberth | Understanding robotic process automation (RPA)[END_REF]. In addition, Willcocks et al.

(2017) argue that programming skills are not necessary to implement RPA, and buyers and others involved in the procurement process can easily learn how to use RP robots.

Table 1, provides summary of extant research about motives and challenges that individuals and organizations could face in the context of RPA implementation.

To minimize or avoid human errors such as mistakes when confirming business information (amounts, contracts information…) (Huang and Vasarhelyi, 2019) Quality and work accuracy (Lacity and Willcocks, 2015) with procurement more focused on strategic decisions and activities [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF].

Save the accountant's time (Lacity and Willcocks, 2015) No computer programming skills are needed (Willcocks et al., 2017)

Individual motives

Multi-tasking and switching to other processes quickly (Lacity and Willcocks, 2015) Capacity to work at any time (Kanellou and Spathis, 2013) Widely adopted in many industries due to its low implementation costs and high potential benefits (Huang and Vasarhelyi, 2019)

Organizational motives

Reduces the number of workers (Lacity and Willcocks, 2015) 1 Procurement is joining the dots with technology to achieve greater strategic benefits, Wax digital study 2018. 2 Industry 4.0: Building the digital enterprise, pwc 2016.

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Organizations can deal with the rapidly increasing amount of data (Lacity and Willcocks, 2015) Technology induces fears in workers [START_REF] Smith | AI, Robotics, and the Future of Jobs[END_REF] Fear of sharing appropriate information with partners (concerns about falling behind their competitors or of being exploited by their partners), [START_REF] Preindl | Transformation strategies for the supply chain: the impact of industry 4.0 and digital transformation[END_REF].

Individual difficulties

Changing tasks and roles (IRPA, 2014) Lack of planning [START_REF] Büyüközkan | Digital Supply Chain: Literature review and a proposed framework for future research[END_REF] Requires proactive planning (Lacity and Willcocks, 2015) The project needs to be supported by top management and information technology (Lacity and Willcocks, 2015) and Kosmol et al., (2019) Organizations need to consider their management and leadership as an essential function in the digital transformation process [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF].

The digital transformation roadmap requires involvement at all organizational levels and by all decision-makers (Issa et al., 2018).

Organizational challenges

It needs to be both prepared and overseen by humans (Lacity and Willcocks, 2015)

Research methodology

Digitalization in procurement, particularly RPA, has been little studied in the literature. The aim of our research paper is therefore to explore the practical implications and roles of these tools within the procurement function to improve the conceptual understanding of this contemporary issue. Case studies have been adopted as the most appropriate approach to exploratory qualitative research [START_REF] Yin | Case Study Research: Design and Methods, Fourth Edition[END_REF] as empirical research examines issues in a reallife context. Furthermore, we can justify the use of this qualitative method in the same way as the main research questions in 'how' form (Yin, 1994: 6).

We examined multiple examples to select typical cases within a certain domain [START_REF] Eisenhardt | Agency theory: An assessment and review[END_REF]. This research approach can make a useful contribution to both management practices and theory building in a relatively new research field [START_REF] Seuring | Case study research in supply chains -an outline and three examples[END_REF].

We conducted our analysis in seven firms Table .2, that were selected based on their significant interest and success stories on the impact of digitalization in the procurement department. We organized the case selection through a sampling approach using a comparable source (companies based in France) to ensure a uniform exogenous setting. The selected companies needed to have a recognized experience within digitalisation and more precisely RPA implementation in procurement department. We chose companies that differed

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in terms of size and diverse products/ services. Thus, we were able to compare and generalise the results obtained. We identified the main actors in the digitalization process, and the participants in each function involved. For reasons of confidence, the names of the companies in the cases will remain anonymous.

Firm A: Automotive

Firm A belongs to the international Group, one of the world's leading manufacturers of heavy trucks and diesel engines. Table 1.

Presentation of selected firms and experts

The data was gathered from primary and secondary sources. Our approach enabled us to obtain multiple views (procurement manager, buyer, project manager and leader, technical expert). It also provided us with more objective responses to the questions and allowed us to compare answers from different perspectives.

The secondary data came from the company websites as well as internal documents (project

Firm A Firm A Firm A Firm B Firm B Firm C Firm C Firm C Firm D Firm E Firm F Firm G Firm G Firm G
14 respondents 17 interviews Table 3. Respondents' individual profiles listed including title, sectors, annual sales and interview duration.

We conducted 7 semi-structured face-to-face interviews and 10 interviews by phone with the main actors involved in digitalization and RPA in the procurement process. Semi-directive interviews are useful to "understand organizational reality from the point of view of the actors who experience it" (Demers in Giordano, 2003, p. 174.). Each interview lasted an average of one hour. The interviews were recorded and fully transcribed. We followed the recommendations of Miles and Huberman (2003) and decided to stop the coding phase when we reached data saturation. The data was then analysed using an inductive approach. The semi-structured interview guideline was developed from the existing literature on digital

procurement and included open-ended questions to enable knowledgeable respondents to describe events and processes. Some aspects of this interview were more or less developed depending on the interviewee knowledge and his position.

Research findings

We summarized the results of our research as three main findings, in keeping with the key elements of the interview guide.

Attributes for procurement automation

Preparing the ground for the implementation of the automation process is essential. We tried to understand the necessary attributes prior to adoption of RPA in our interviews. The global purchasing director and the digitalization expert from Firm B indicated that the main attribute in the tool's implementation was the use of clear, well-defined and immutable processes.

All the respondents from firms A, B, C and D explained that their procurement department developed a well-established digitalization plan for the procurement function.

For example, Firm B created the position of Digital Chief Transformer (DCT) who is in direct contact with the company's management team and the developers. In this way, the DCT has a clear vision of the company's objectives and can thus use its network of developers to introduce digital tools best adapted to the company's needs.

Firm C also had a strong desire to digitalize. This was supported by the group's purchasing director who created a department dedicated to digitalizing some procurement activities and process improvements. Firm C shared some key documents from the project and related internal communication with us.

Many of the respondents emphasized the importance of the involvement and leadership of the head of procurement in the digitalization project.

"We always start from the user or the operational need to implement these solutions.

Without strong leadership from the Purchasing Director, it won't work! "Accounts director, Firm G. Finally, we should remember that in all the cases studied, procurement is not an isolated field.

"It's crucial, procurement is incorporated prior to rollout of the RPA project and works closely with all the main stakeholders every day." Project manager, Firm A.

The influence of the head of procurement on the success of digitalization was acknowledged, encouraging teams to be more committed and end-users to adopt the new tool.

"The tools are already quite complex. For us, it's important to get the user to enjoy the experience in order to accept it. Buyers can spend a lot of time on tasks that don't give them any real gains" Accounts director, Firm G.

The complexity of tools is clearly viewed as a potential barrier to their use, even if they can be a source of added value for buyers.

"Much more information is available just in time, and the head of procurement wants to deal with it instantly and be sure of getting the right data as well!" Procurement director, Firm A.

Contribution of RPA to the operational purchasing process

The interviewees raised the importance of the role played by RPA to improve the operational activities of procurement agents.

"RPA is like a digital staff member, it's able to use the work environment."

Purchasing director, Firm E.

"For me, process automation is part of a coherent strategy of operational excellence!" Strategic accounts consulting manager, Firm G.

• Time management and optimization

Management of non-productive tasks

When observing the time saved by the implementation of a robot, we can suppose that this could have an impact not only on recruitment strategy but also on potential job losses in some companies. To our surprise, this hypothesis has been strongly disproved by the interviewee. The head of site purchasing in Firm D and the purchasing director in Firm E pointed out that the cost of managing category C purchases is far too high in relation to the added value. Thus, the reduction in time and resources devoted to managing these purchase categories is a much appreciated gain for buyers.

"Priority is given to manual and redundant buyer tasks to offload the teams and allow them to concentrate on more value-added tasks." Secondary data: project documentation from purchasing director, Firm C.

The digitalization expert in Firm A noted that employees have to do difficult and nonproductive tasks. Releasing staff from these tasks therefore frees up resources to concentrate on more value-creating jobs. Even though, RPA "We start with RPA and always begin with the simplest buyer tasks, like creating supplier profiles on SAP, for instance." Account director, Firm G.

Processing times and flexibility

The procurement manager described the problems linked to the processing times of supplier invoices. The 2008 LME law obliges companies to pay their suppliers within 60 days to avert business failures.

"We set up a robot that could mimic the manual process of a person copying and pasting data from one source to another. With this technology, the processing time for supplier invoice payments was reduced from 24 hours to 1 hour." Secondary data: testimonial of the purchasing director from project documentation and communication, Firm C.

The respondents also explained that companies sometimes have difficulty speeding up the processing of supplier invoices, not because of unwillingness, but because of excessively long reception times (arrival time of letters via the post or because suppliers send their invoices too late) and the lack of technical and human resources. Thus, financial transactions are time-consuming and expensive as they often require several intermediaries. Respondents from firms A, B and C felt that there is still a lot of progress to be made in this area. Back-end management of invoices can be done by the robot, thereby accelerating the process.

This improvement would also impact on the procurement agent's time management.

Moreover, robots can operate 24 hours a day, 7 days a week, and adapt to high workloads.

• Improvement/automation of internal processes "Input errors are numerous and generate significant costs and expenses to correct them" Purchasing director, Firm E.

In addition to saving time for buyers and money for the company, RPA helps to minimize human errors if the robot is properly configured as it will not make mistakes due to inattention or fatigue.

"Today, a buyer has no added value in processing invoices, when invoices can be processed by machines." Account manager, Firm G. As invoice management, creation of supplier data on SAP and supplier base management in general is managed through these systems, RPA speeds up processing and avoids human error, so the operational quality of the process improves. This in turn has a strong impact on the suppliers' perception of the quality of the buyer's company. "In the end, it improves the perceived quality of service and therefore the satisfaction of stakeholders! Suppliers really appreciate it and feel that procurement agents are more professional." Procurement expert and owner, Firm F.

The respondents highlighted some of the positive effects on supplier relationship management due to the operational efficiency (data accuracy and integrity, just in time information…).

"It helps to develop a trust-based relationship with our key strategic suppliers, particularly given the accuracy of the information we share." Procurement project manager, Firm A.

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Motivations to adopt RPA During the interview, the respondents noted that the motives behind RPA adoption are clear enough for companies. These motives differ from one organization to another, depending on each one's need.

• Return on investment: Cost improvement

According to the respondents interviewed, the main motives and challenges are economic.

Moreover, some procurement heads expected to make a profit from investing in the technology.

"The advantage is that you only train the robot once! Moreover, there was no need to train 10 people." Procurement, Firm C.

• Reviewing and clarifying the procurement process

Sometimes the procurement process is not clear internally or it changes and is not documented, and since RPA is a useful tool for process improvement, it can help managers to review their processes and standardize them. "There's still no real process documentation in companies." Accounts director, Firm G.

• Sorting and accuracy of data

The interviewees agreed that there is often a high volume of data to deal with and the activity done by employees may not have any added value. However, at the same time, keeping data on a system costs the company a lot of money as they pay an external provider for data maintenance. Stakeholders therefore realized that in addition to an acceptable ROI, RPA helps to clean the data, ultimately saving money.

"The CFO and the purchasing manager decided to review the dataset, although at the beginning that was not the objective... it was to relieve the person in terms of workload so that he could focus on his job." Accounts director, Firm G.

"Surprisingly, ROI was not the sole driver in the end!" Global purchasing director, Firm B.

Main difficulties encountered

The interviewees pointed to the challenges regarding changing employees' habits and convincing them that robots would help them to do their job better. Also, most of the time, the business processes were partially described but not stable enough.

"But, we've always done it this way…" Purchaser, Firm C.

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The fact that automation for the purchasing process is not applicable in all purchasing categories was mentioned. Some buying categories are complex due to the large number of suppliers. Most of the time, the focus is on strategic suppliers rather than suppliers that are often replaced.

"It works less well on indirect purchases because there are too many suppliers, it's a much more complex process." IT expert, Firm A.

Discussion

We remind that most studies are only beginning to explore the effects of advanced digital technologies on procurement field (e.g., Kache and Seuring, 2017). The attributes, motives and difficulties inherent in RPA adoption emerged from our case studies of companies using this process for procurement purposes.

The interviewees pointed to the importance of the preparation stage of RPA adoption, identifying certain factors that ensure successful procurement automation before RPA implementation. All the cases indicate that companies must have a certain degree of flexibility, but also an infrastructure that allows for the implementation of such tools. The potential of all their IT tool functions is not fully exploited, especially in RPA and IA in procurement.

Our findings show that process maturity is one of the key factors in the adoption of RPA.

Procurement maturity is referred to as "the professionalism levels of the procurement function" Rozemeijer et al., (2003). The procurement director's involvement in the upstream process of project implementation is a sign of the function's maturity in the company and appears to be a key requirement. Moreover, in order to succeed in this area, organizations need to provide training and define a common mindset towards the digital transformation [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF]. This helps to overcome individual challenges (trust in technology…) for the adoption of these digital tools [START_REF] Boughzala | Factors of public e-procurement adoption: An Exploratory Field study with French practitioners[END_REF].

Table 4. below demonstrates and summarizes the main results concerning individual motives and difficulties from a purchasing manager's perspective as well as the organizational motives and challenges related to RPA implementation.

Individual motives

To minimize or avoid human errors such as mistakes when confirming business information (amounts, contracts information…) (Huang and Vasarhelyi, 2019) Quality and work accuracy (Lacity and Willcocks, 2015) with procurement more focused on strategic decisions and activities [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF].

Help and relieve the procurement agent in terms of workload

Partly observed

No evidence not the final objective

Newly observed

Organizational motives

Organizations can deal with the rapidly increasing amount of data (Lacity and Willcocks, 2015) Partly observed

Technology creates fear in workers [START_REF] Smith | AI, Robotics, and the Future of Jobs[END_REF] Fear of sharing appropriate information with partners (concerns about falling behind their competitors or of being exploited by their partners), [START_REF] Preindl | Transformation strategies for the supply chain: the impact of industry 4.0 and digital transformation[END_REF].

Fear of changing working habits

Issues in change management

Not confirmed

Newly observed

Individual difficulties

Changing tasks and roles (IRPA, 2014) Partly observed

Lack of planning [START_REF] Büyüközkan | Digital Supply Chain: Literature review and a proposed framework for future research[END_REF] Requires proactive planning (Lacity and Willcocks, 2015) Partly observed

The project needs to be supported by top management and information technology (Lacity and Willcocks, 2015) and Kosmol et al., (2019) Organizations need to consider their management and leadership as an essential function in the digital transformation process [START_REF] Bienhaus | Procurement 4.0: factors influencing the digitisation of procurement and supply chains[END_REF].

The digital transformation roadmap requires involvement at all organizational levels and by all decision-makers (Issa et al., 2018).

Led by procurement director

Maturity and recognition of procurement function is needed

Partly observed

Partly observed

Confirmed

Newly observed

Newly observed Our results are also closely linked to recent work on RPA implementation by [START_REF] Rutaganda | Avoiding pitfalls and unlocking real business value with RPA[END_REF]. The main reason for the failure of RPA input is that organizations overestimate the potential gains. Indeed, management is sometimes disappointed with the mismatch between the gains they expected and the actual gains. The second obstacle to the successful introduction of RPA, which we highlight in our research, is the need for clear and welldefined business processes. Further research on the upstream involvement of the procurement function in digitalization is therefore required.

Organizational challenges

The procurement manager highlighted the issues linked to the processing times of supplier invoices. The respondents also explained that companies sometimes have difficulty speeding up the processing of supplier invoices, not because of unwillingness, but because of excessively long reception times and a lack of technical and human resources.

This issue of payment delay is rarely raised in literature. However, professionals point to one of the problems that they believe impacts on their ability to pay on time.

The findings from the case studies reveal that RPA frees the buyer from non-value added tasks and improves their operational efficiency, but also their legitimacy. The following processes in the procurement function can be automated, such as, contract deployment, invoicing management or updating supplier data such as bank or address data. The RPA made it possible to formalize the process and keep the knowledge.

Beyond a function that can be seen as administrative, the Purchasing department plays the role of intermediary with suppliers, ensuring the conformity of offers with the initial specifications, negotiating and supervising the implementation of contracts (Van Weele, 2010). Many of the cases concur that RPA contributes to stakeholder satisfaction and the quality of the buyer-supplier relationship. As the respondents noted, in order to be strategic, procurement activities must include an analysis of market trends, providing an opportunity for procurement managers to act ahead of time and anticipate future innovations produced by suppliers.

However, it is well known that the quality of the buyer-supplier relationship is a crucial factor in meeting this challenging goal. The procurement department is particularly well positioned to identify new market ideas (Servajean-Hilst and Calvi, 2018) through their frequent

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interactions with the supplier base. When a relationship of trust is established between buyers and suppliers, and thanks to a recognized legitimacy, the latter are more likely to develop new ideas with the buyer [START_REF] Homfeldt | Identification and Generation of Innovative Ideas in the Procurement of the Automotive Industry: The Case of AUDI AG[END_REF].

Through this study we expand to Srai and Lorenz (2019) research, which suggesting an approach to designing digitalization interventions in Purchasing and supply management functions, by defining why (here the motives) and how companies proceed to set up RPA in the procurement function.

Conclusion, limitations and future avenue

In order to summarize the findings of our research paper and move towards a future research agenda, we first identify the implications from this study on digital procurement and then consider its limitations.

With the emergence of new technologies, the buyer's activity is changing rapidly as a result of digitalization. This led us to examine how digitization, and RPA in particular, can help buyers to provide added value for their companies.

The study makes several contributions. First, it provides empirical evidence of the attributes for procurement automation. Some attributes found in the literature have been confirmed

(like unambiguous rules, limited exception handling, high and predictable volumes, stable environment, accessed multiple systems), while other attributes have been explored below:

-The project needs to be led by the procurement director, -A well-established digitalization plan is required for the procurement function.

Second, we outline some of the major impacts of RPA on traditional procurement activities.

The study suggests that RPA has an impact on procurement from an operational (time management and optimization, processing flexibility and improvement/automation of internal processes), organizational (impact on the procurement team, new roles) and relational (stakeholder satisfaction and quality of the buyer-supplier relationship) perspective.

Finally, the study offers clearer insights into the motives for adopting RPA through important, newly observed aspects of the procurement process, such as:

-Allowing procurement agents to focus on value-added tasks and increase their legitimacy, -Helping and relieving the procurement agent in terms of workload, -RPA does not necessarily lead to job cut (FTE),

We also explored the difficulties encountered by the project members:

-Some procurement agents are afraid of changing their working habits, (1993, p 6). RPA and IA could help procurement agents to address this challenge. We believe that this could be addressed in future research.

Some limitations also need to be considered in this exploratory work. We recommend expanding the research focus by using longitudinal analyses and other cases for additional comparative studies. Furthermore, while we asked the main respondents about the challenges encountered, our research focused mainly on the positive impact of RPA in the procurement process. It would be useful to counterbalance these results in the light of negative impacts.

Indeed, it would be interesting to focus on internal and external resistance to change management linked to RPA and IA. Managing resistance to change can be considered as one of a key success factor for implementing purchasing groups [START_REF] Bentahar | Key success factors for implementing purchasing groups in the healthcare sector[END_REF].

From a research design perspective, we did not ask the suppliers for their views on the question of RPA adoption and satisfaction. Even though the interviewees' perceptions were taken as 'a realistic view', this presents an unbalanced perspective of the research object, which could be perceived as biased.

  , organizations have had to follow the 4.0 industrial revolution, especially digitalization, very closely because of the impact it can have on the success or failure of organizational matters like profitability and competitiveness.

  Harthley et al. (2019) recommends that companies ensure their readiness to adopt and effectively use one or more of these technologies. The process would involve: (1) identifying an SC technology visionary who can lead the way through the maze of technologies and the changing digital landscape, (2) developing a digital technology roadmap for the SC processes, and (3) updating foundation information systems.

  The procurement strategy aims to develop our suppliers as partners. The collaboration drives growth, profitability and continuous improvements with a focus on customer success. The development of digitalization is taking place at striking speed. Internal secondary sources (intranet and internal reporting project) Firm B: Leader in sustainable mobility B specializes in the manufacture and marketing of tires. The company strives for operational excellence. The digitalization and simplification supported by competitiveness plans are powerful levers to improve operations (SCM, purchasing, production...). 85% of purchases are centralized and the function is seen as mature. Secondary sources (external website, internal reporting from intranet) Firm C: European MRO and business supplies dealer The value of Firm C, as a distributor, is putting sincerity and responsibility at the heart of all our actions. C offers a wide range of quick and easy solutions based on select products and services. For C, technology frees up time for creating value throughout the procurement cycle. Secondary sources (external website, internal reporting from intranet) Firm D: Pharmaceutical Firm D is fourth largest subsidiary of a biopharmaceutical company founded over 140 years ago. Digitalization is not new at D, automation systems that appeared in production in the 1980s, the company has been involved in electronic document management for more than 20 years.

•

  The interview guide is structured as follows: General overview • Brief overview about the company • description of your role and your responsibilities Digital procurement /RPA project Major attributes and enablers for the procurement process automation • Company's motives in adopting RPA in procurement process • What are different challenges faced by the team / company before and during the implementation journey • Major impacts of RPA implementation on procurement activities and process Closing Questions and summaries

"

  Productivity gains can improve the legitimacy of procurement departments... and this legitimacy is strategic, so I am more than implicated in such transformation!.", Purchasing director, Firm C.

"

  The objective of the RPA implementation is really about the improvement of the process and we have no plans to reduce resources and save full time equivalent (FTEs)" Purchasing director, Firm E.

"

  With this technology, the accuracy rate increased from 97% to 100%." Secondary data: testimonial of purchasing director from project documentation and communication, Firm C. • Stakeholder satisfaction and quality of buyer-supplier relationship "Digitalization is transforming the way we work, innovate and collaborate. It allows to manage the company from more decentralized way and to maintain a richer relationship with our customers and suppliers." Secondary data: public reference document posted in march 2019, Firm B.

-

  Need to launch a change management program, -Maturity and recognition of the procurement function is needed as a prerequisite. However, the potential of all the tool functions has not been fully exploited, especially in RPA and IA in the procurement sector. O'Neal (1993) considers procurement agents as "informants" of potential innovative supplier projects. "Marketing scans the environment in search of unfulfilled needs, R&D develops new techniques/technologies and Purchasing acquires, assimilates, digests and shares information on new and potential supplier developments" O'Neal

  IEEE Corporate Advisory Group, 2017, cited by Huang et al., 2019 p. 3).

	RPA concerns tools "designed to be used by subject matter experts to automate tasks that use
	rules to process structured data, resulting in a single correct answer and deterministic
	outcome" (Lacity and Willcocks
	F o r
	R
	e v
	i
	e
	w
	O
	n l y
	7

Table 1 -

 1 RPA's individual and organizational issues

  the collected primary data results can be compared to. The combination of the data collected enables us to assume internal validity.

	Company Respondents' positions/Title	Sector / Firm Annual Sales	Interview
		2018	duration
	Procurement manager (direct)	Automotive / 309 millions euros	1h
	Project manager		1h15
	IT expert		2h45 (2 interviews)
	Global purchasing director	Leader in sustainable mobility /	30 mins
	Expert in digitalization	22.03 billion euros	50 mins
	Purchasing director	European MRO and business	1h15
	Procurement project manager	supplies dealer / 774 millions	1h
	Purchaser	euros	45 mins
	Head of site purchasing F Purchasing director o r Procurement expert and owner	Pharmaceuticals / 3,1 billion euros Energy / 57 millions of euros Consulting in procurement / 3,5	1h15 1h55 (2 interviews) 1h
	Accounts director IA project leader National sales director R v millions of euros Strategic IT consulting / 4,1 millions of euros e	1h30 2h30 (2 interviews) 45 mins
		i
		e
		w
		O
		n l y

documentation, examples of success stories/quick wins, digitalization/RPA training materials). The data also covered field observations of the companies we visited. In addition, this process resulted in a verification study of the interviews. Secondary data can be helpful in the research design of subsequent primary research and can provide a baseline with which

Focus on added-value tasks Helping in increasing buyer legitimacy Newly observed Newly observed

  Save the accountant's time(Lacity and Willcocks, 2015)

	F o r
	R
	e v
	i
	e
	w
	O
	n l y
	Confirmed
	Confirmed

Save procurement agent's time RPA does not necessarily lead to job cut (FTE)

		Confirmed
		Newly observed
	No computer programming skills are needed (Willcocks et	Not confirmed
	al., 2017)
	Multi-tasking and switching to other processes quickly	Confirmed
	(Lacity and Willcocks, 2015)
	Capacity to work at any time (Kanellou and Spathis, 2013)	Confirmed
	Widely adopted in many industries due to its low
	implementation costs and high potential benefits (Huang
	and Vasarhelyi, 2019)
	Reduces the number of workers (Lacity and Willcocks,
	2015)

Change management applied to process and employees' habits Newly observed RPA is less applicable in indirect procurement categories (due to complexity) Newly observedTable 4 .

 4 Individual and organizational motives and difficulties.

	F o r
	R
	e v
	i
	e
	w
	O
	n l y
	It needs to be both prepared and overseen by humans	Partly observed
	(Lacity and Willcocks, 2015)
		20

https://mc04.manuscriptcentral.com/scfij Email: TSCF-peerreview@journals.tandf.co.uk Supply Chain Forum: an International Journal

 Huang, F., & Vasarhelyi, M. A. (2019)


Mohammed Bey

Ahmed Khernache

Jalil Boukhobza

Daniel Menard

Energy-Aware HEVC Software Decoding On Mobile Heterogeneous Multi-Cores Architectures

Keywords: 2012 ACM Subject Classification Hardware → Platform power issues, Hardware → Chip-level power issues, Computing methodologies → Classification and regression trees, Computer systems organization → Multicore architectures energy consumption, mobile platform, heterogeneous architecture, software video decoding, hardware video decoding, HEVC Digital Object Identifier 10 .PARMA-DITAM.2022

Video content is becoming increasingly omnipresent on mobile platforms thanks to advances in mobile heterogeneous architectures. These platforms typically include limited rechargeable batteries which do not improve as fast as video content. Most state-of-the-art studies proposed solutions based on parallelism to exploit the GPP heterogeneity and DVFS to scale up/down the GPP frequency based on the video workload. However, some studies assume to have information about the workload before to start decoding. Others do not exploit the asymmetry character of recent mobile architectures. To address these two challenges, we propose a solution based on classification and frequency scaling. First, a model to classify frames based on their type and size is built during design-time. Second, this model is applied for each frame to decide which GPP cores will decode it. Third, the frequency of the chosen GPP cores is dynamically adjusted based on the output buffer size. Experiments on real-world mobile platforms show that the proposed solution can save more than 20% of energy (mJ/Frame) compared to the Ondemand Linux governor with less than 5% of miss-rate. Moreover, it needs less than one second of decoding to enter the stable state and the overhead represents less than 1% of the frame decoding time.

Introduction

Mobile video content will generate nearly four-fifths of mobile data traffic by 2022, according to Cisco [START_REF]Cisco Visual Networking Index: Forecast and Trends, 2017-2022 White Paper -Cisco[END_REF]. Smartphones, tablets, and media players are the favored and most frequentlyused tools to consume this multimedia content. This proliferation can be explained by the omnipresence of mobile devices which made the consumption of these data easier. Also, the Covid-19 crisis has soared the use of video content, e.g., video-conferencing [10]. This context made the energy efficiency one of the most important factors in modern mobile platforms design, in particular for video decoding applications. To reduce video decoding energy consumption while delivering high performance, one proposed solution is the use of hardware (HW) video decoding performed by a HW decoder Intellectual Property (HDIP), such as the HEVC decoder [START_REF] Tikekar | A 249-mpixel/s hevc videodecoder chip for 4k ultra-hd applications[END_REF][START_REF] Amish | Fully pipelined real time hardware solution for high efficiency video coding (hevc) intra prediction[END_REF]. Actually, in a state-of-the-art work, dedicated processors outperform general purpose processors (GPPs) by around 1000× in terms of energy efficiency [START_REF] Horowitz | 1.1 computing's energy problem (and what we can do about it)[END_REF]. As a consequence, most modern smartphones are equipped with an HDIP [START_REF] Xu | Methods for power/throughput/area optimization of h.264/avc decoding[END_REF][START_REF] Moyer | Chapter 13 -hardware accelerators[END_REF]. However, HDIPs are not flexible and are costly to implement, which generate a long time-to-market for new video codecs [START_REF] Choi | Leveraging parallel computing in modern video coding standards[END_REF].

Heterogeneous multi-cores GPPs embedded in mobile platforms are composed of cores of different performances, e.g., ARM big.LITTLE architecture processors. They offer a great opportunity to enhance both performance and energy efficiency of software (SW) video decoding using parallelism and frequency scaling. In particular, HEVC is a parallel-friendly video codec as it supports different parallelism schemes [START_REF] Sjoberg | Overview of hevc high-level syntax and reference picture management[END_REF]. Furthermore, GPPs are flexible as they allow developing and rapidly deploying new codecs. Therefore, if well exploited, GPPs energy consumption may be as close as possible to that of the HDIP of the target platform while satisfying the real-time decoding constraint.

To exploit the heterogeneity of modern GPPs, one should balance the video frames workload among GPP cores carefully. However, video frames, in a same video sequence, have different complexities. In addition, predicting the complexity of a frame is a challenging task as the information about it are normally not known before to start the decoding process, except at the crude level of whether a frame is of type I/P/B and its size.

Dynamic voltage and frequency scaling (DVFS) is a technique that addresses the variability of the video workload to reduce the consumed energy. It is enabled by scaling down/up the voltage (and frequency) based on the frame complexity. However, despite the possibility of decoding each video frame at a different frequency, Jensen's inequality [START_REF] Im | Dynamic voltage scheduling with buffers in low-power multimedia applications[END_REF] shows that decoding several frames at the average frequency gives better energy efficiency than decoding each frame at a different frequency.

The research questions (RQ) addressed in this paper are as follows:

1. RQ1: How to balance the video frames among the heterogeneous GPP cores, based on a little amount of information on the video frame to decode ?

2. RQ2: Once the GPP cores are selected, how to adjust their frequency in order to reduce the energy consumption ?

In this paper, we propose a solution composed of three phases:

1. Modeling of frame complexity: to establish a model able to classify video frames into two groups: (i) most complex frames, and (ii) least complex frames.

2.

Assignment of frames using classification: to decide to which GPP cores (high performance or energy-efficient) a frame should be submitted to. This phase solves RQ1.

3.

Frequency scaling using feedback control (PI controller) with DVFS: to monitor the output buffer size in order to adjust the GPP frequency, here we reused the work in [START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF]. This phase solves RQ2.

The results show that, on the tested platforms, the proposed solution can save on average more than 20% of energy (mJ/Frame) compared to the Ondemand Linux governor. Moreover, the classification allows to exploit the heterogeneity of ARM big.LITTLE architecture by limiting the miss-rate to less than 5% of decoded frames. Finally, the proposed solution is very light as it represents on overage less than 1% of the frame decoding time.

Section 2 gives some background. Then, Section 3 reviews some related work. Our contribution is described in Section 4 with experimental results in Section 5. Finally, we conclude in Section 6.

Background

HW video decoding

HDIPs are massively parallel. Their architectures have been optimized for such parallelism by eliminating the power consumption related to instruction decoding and control logic characterizing GPPs [START_REF] Hameed | Understanding sources of inefficiency in general-purpose chips[END_REF]. For example, they integrate extreme multi-threading HW or specific data handling and memory access optimization HW [START_REF]Development of the VPU[END_REF]. The main advantage of such accelerators is their energy efficiency. For that, video decoding functions, in general, exhibit massive data parallelism thanks to some schemes proposed by video codecs.

The GPP generally communicates with the HDIP as an input/output (I/O) operation. This inter processor communication (IPC) may generate some energy overhead [START_REF] Golston | Optimized video decoder architecture for TMS320C64x DSP generation[END_REF][START_REF] Li | An integrated hardware/software design methodology for signal processing systems[END_REF]. The IPC also includes all other elements involved in the HW video decoding such as memory transfers. When the HDIP is called to proceed with the decoding process, the GPP may enter the idle state and needs to handle the HW interrupt. This also generates some energy overhead.

SW parallel processing

To understand how architectural strategies can provide high processing performance at low power levels, it is necessary to look at the CMOS circuit dynamic power consumption equation. The dynamic energy of a CMOS circuit can be formulated as:

E dyn = P dyn * t
(1)

P dyn = K.C eff .f.V 2 dd ( 2
)
where P dyn is the dynamic power, K is a constant, C eff is the circuit effective capacitance, f is the circuit clock frequency, and V dd is the circuit voltage [START_REF] Burd | Energy efficient cmos microprocessor design[END_REF].

For instance, running a process using 2 GPP cores clocked at f 2 can save 2× of the consumed energy compared to using 2 GPP cores clocked at f . More energy can be saved in the case where the voltage V dd is scaled with the frequency f . Therefore, by decreasing the frequency to the lowest level that provides the required performance, one can significantly reduce the consumed energy.

Related work

The studies conducted on video decoding energy consumption can be grouped according to the decoding parallelism scheme: (i) tiling, (ii) wavefront parallel processing (WPP), and (iii) frame-by-frame.

Tiling parallelism scheme

This scheme is supported by HEVC. In [START_REF] Yoo | Parallel hevc decoding with asymmetric mobile multicores[END_REF][START_REF] Roh | Prediction complexity-based hevc parallel processing for asymmetric multicores[END_REF][START_REF] Baik | A complexity-based adaptive tile partitioning algorithm for hevc decoder parallelization[END_REF], the proposed solutions consist in scheduling frame tiles among heterogeneous cores, in a mobile asymmetric multi-cores architecture, e.g., ARM big.LITTLE. The scheduling is based on the tile complexity and the performance ratio between big and LITTLE cores. The tile complexity can be estimated by its resolution, the number of PUs that it incorporates, or the number of bits encoded in each CTU1 of the tile.

4:4

Energy-Aware HEVC SW Decoding

WPP parallelism scheme

HEVC supports also the WPP parallelism scheme. In [START_REF] Rodríguez-Sánchez | Architecture-aware optimization of an hevc decoder on asymmetric multicore processors[END_REF], the authors developed a strategy based on task migration between big and LITTLE cores such that all cores are busy all the time. In [START_REF] Chi | Parallel scalability and efficiency of hevc parallelization approaches[END_REF], the authors proposed an approach called Overlapped WaveFront (OWF) that can be implemented on top of WPP. The proposed decoder consists of three pipeline stages: parse, issue, and output. Each of these stages is performed by a different thread.

The proposed solution (OWF) achieves higher performance and scalability than both WPP and tiling.

Frame-by-frame parallelism scheme

Most state-of-the-art studies exploited the frame-by-frame parallelism scheme to reduce energy consumption of SW video decoding. For instance, in [START_REF] Nogues | A dvfs based hevc decoder for energy-efficient software implementation on embedded processors[END_REF], the authors proposed a solution that dynamically adapts the processing frequency to the video frames characteristics. The complexity of the (L + 1) th frame is estimated by the average decoding time of the last L decoded frames, L being a parameter.

In [START_REF] Nogues | Convex energy optimization of streaming applications for mpsocs[END_REF], the authors introduced a method that determines the most energy efficient operating point in terms of GPP frequency and number of GPP active cores in a mobile multi-processor SoC (MPSoC) to perform HEVC decoding. The proposed method jointly considers the DPM, DVFS, and parallelism capabilities of, on the one hand, the targeted MPSoC and, on the other hand, the HEVC application. In [START_REF] Duan | Novel efficient hevc decoding solution on general-purpose processors[END_REF][START_REF] Chi | Low-power high-efficiency video decoding using general-purpose processors[END_REF], the authors exploited the SIMD and multi-threading to decode multiple frames in parallel, in addition to low-power states that reduce the active and idle powers.

Approximate computing can also be used to save energy when performing video decoding [START_REF] Nogues | Algorithmic-level approximate computing applied to energy efficient hevc decoding[END_REF]. It consists in skipping some modules or replacing them by others of lower complexity.

In [START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF], the authors proposed a solution to reduce the energy consumption of video decoding using a PI controller. The proposed solution controls the GPP frequency based on the output buffer size, i.e., the number of decoded frames waiting for display. The GPP frequency is scaled up or down depending on the buffer size and the display rate. This technique were reused in our contribution.

Discussion

Contribution

In this paper, we propose a solution to reduce the energy consumption of HEVC SW decoding on mobile platforms. It aims at diminishing the HEVC SW decoding energy consumption to be as close as possible to that of the HEVC HDIP of the target platform while satisfying the real-time decoding constraint. This is achieved by two mechanisms: parallelism and DVFS. Our proposed solution includes three different phases: (1) modeling of frame complexity, (2) assignment of frames to appropriate GPP cores using classification, and (3) frequency scaling using a PI controller with DVFS. Fig. 1 depicts an overview of the proposed solution. The first phase is performed offline, i.e., during design time, whereas the two other phases are performed online, i.e., during the decoding process. Phase 2 solves RQ1 and phase 3 solves RQ2.

Phase 1: Modeling of frame complexity

The objective of the first phase is to build a model of frame complexity which is able to balance the video frames workload between the high performance and energy-efficient GPP cores. Indeed, this work focuses on heterogeneous processors containing high performance cores and energy-efficient ones (such as ARM big.LITTLE processors). For that, any given video frame is classified into two groups: (i) most complex frames to be decoded by high performance GPP cores, and (ii) least complex frames to be decoded by energy-efficient GPP cores. The complexity is expressed as the number of GPP clock cycles required to decode a frame.

To build the model of frame complexity, as illustrated in Fig. 1, there are two steps: (a) training of the model, and (b) validation of the model. In the first step, data related to frames representing multiple video sequences are collected. Then, a part of them (70%) is injected to the model for training, i.e., the model takes those data to learn how to correlate the input parameters to the output one which is the frame complexity. In the second step, the model is applied on the remaining data (30%) to evaluate its accuracy.

Fig. 2 depicts the inputs and output of the established model. The output is: (i) the group of most complex frames, or (ii) the group of least complex frames. The inputs are the independent variables (a.k.a. features): frame type and frame size. In a previous work [START_REF] Bavier | Predicting mpeg execution times. SIGMET-RICS Perform[END_REF], it has been shown that these two parameters are very correlated to the frame complexity in MPEG video codec. However, in case of HEVC, our experiments revealed a weak correlation (R 2 = 0.55). As a result, we added three configuration parameters to improve the accuracy of the model.

The added parameters are: (i) video bitrate, (ii) video frame rate, and (iii) the performance ratio between the high performance and energy-efficient GPP cores. First, it has been shown that the video bitrate is correlated with the video decoding energy consumption in case of HEVC SW decoding [START_REF] Bey Ahmed Khernache | Hevc hardware vs software decoding: An objective energy consumption analysis and comparison[END_REF]. Second, the video frame rate is used to determine the frame decoding deadline. Finally, the performance ratio is used as the GPP cores offer heterogeneous performances.

Finally, the regression model used to train the video frames data is the logistic regression [START_REF] Kudryashov | Logistic function as solution of many nonlinear differential equations[END_REF]. The reason of this choice is its simplicity of implementation and its efficiency to take a binary decision (most complex or least complex frames) [START_REF] Kudryashov | Logistic function as solution of many nonlinear differential equations[END_REF].

The model resulting from this phase is expressed by the following formula, using a logistic function:

y = 1 1 + e -p(x1,x2,x3) (3)
where y is the output of the model used in phase 2. It takes values between 0 and 1. p(x1, x2, x3) is the linear function of the input and configuration parameters described above. It is a real number. The linear function is formulated as follows:

p(v bitrate , f type , f size ) = w 0 + w 1 * v bitrate + w 2 * f type + w 3 * f size ratio_performance (4)
where w 0 is the intercept (a constant), v bitrate is the the video bitrate, f type and f size are the type and size of the frame to decode, respectively, and ratio_performance is the ratio of performance between the high performance and energy-efficient GPP cores of the target platform. Finally, w 1 , w 2 , and w 3 are the coefficients of the model.

Phase 2: GPP cores assignment using classification

The objective of the second phase is to decide online which GPP cores (high performance or energy-efficient) will decode the next frame. For that, the model built in the previous (offline) phase classifies the frames into two groups: (i) most complex frames, and (ii) least complex frames. The first group is submitted to the high performance GPP cores and the latter to the energy-efficient ones. Then, the frame is decoded in parallel among the selected GPP cores via tiling or WPP parallelism scheme (depending on the coding configurations). This phase is performed during run-time, i.e., while performing video decoding as illustrated in Fig. 1. It is applied for each frame of the input video. The classification is realized using Equation (4). Fig. 3 depicts the algorithm of classification in a graphical representation. For each frame, the classifier function using Equation ( 4) is applied. If the result is positive, the frame is submitted to the high performance GPP cores; otherwise, it is submitted to the energy-efficient GPP cores. Note that if two or more consecutive frames are submitted to the same GPP cores type, they are stored in an input buffer.

Phase 3: Frequency scaling using PI controller [27]

The objective of this phase is to select the clock frequency at which the selected GPP cores will decode the current frame. For that, the Proportional Integral (PI) controller proposed in [START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF] is adopted. This controller monitors the output buffer size in order to maintain it at a desired value (set point) which is an input parameter of the controller. To set the desired value, one can follow the guidelines given in the literature review, such as [START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF][START_REF] Chi | Low-power high-efficiency video decoding using general-purpose processors[END_REF]. This phase is performed during run-time, as illustrated in Fig. 1. It is applied for each frame of the input video.

The PI controller has two inputs: (i) a set point, i.e., the desired output buffer, and (ii) the current output buffer size. Then, according to the output buffer size, the PI controller adjusts the GPP frequency, using DVFS, so that the current buffer size meets the set point. Note that the controller is engaged only when the difference between the set point and the current output buffer size is not zero.

To speed up or slow down the GPP cores, the GPP frequency, gpp_cores_freq, is calculated by multiplying the highest supported GPP cores frequency, GP P _cores max_freq , by a scaling factor, r.

gpp_cores_freq = GP P _cores max_freq * r (5)
The scaling factor, r, is, in turn, decomposed into two components, as illustrated by the following formula:

r(n) = r e (n) + r c (n) (6)
where r e is the scaling factor estimation based on the history of the decoded frames, r c is the output of the PI controller which is considered as an adjustment of r e to compensate the missed deadlines in the past, and n is the number of the next frame to decode. That is, a negative value of r c indicates that the GPP cores should be slowed down, and vice versa.

Evaluation methodology & setup

This section describes the evaluation methodology.

Experimentation setups

The HW and SW experimental setups as well as datasets (33 video sequences) on which the proposed solution was applied are presented and summarized in Table 1. First, the experiments were carried out on two different platforms (Snapdragon 810 and Odroid-xu3 for HW and SW video decoding, respectively). Then, the same experiments were performed on a single platform (RB3) for both HW and SW video decoding.

HEVC HW decoder

Android application + Mediacodec API -Open-HEVC [START_REF]GitHub -OpenHEVC/openHEVC at ffmpeg_update[END_REF] HEVC SW decoder -Open-HEVC [START_REF]GitHub -OpenHEVC/openHEVC at ffmpeg_update[END_REF] Ffmpeg [START_REF]Download FFmpeg[END_REF] + v4l2 library Video sequences datasets : 33 video sequences JCT-VC [START_REF] Bossen | Common test conditions and software reference configurations[END_REF], Jellyfish [START_REF]Jellyfish Bitrate Test Files[END_REF], and some well-known video sequences on the web, e.g., [START_REF]Derf's Test Media Collection[END_REF]. Resolution: 1080p. Frame rate: 25, 30, and 50 fps. Mode: Random Access. Profile: Main

To build the model of phase 1 of the proposed solution, sickit-learn framework [9] was used via Python programming language.

Methodology

We evaluated our solution in four steps.

First, the proposed solution is compared to 5 state-of-the-art solutions, as summarized in Table 2. In the first one, no DVFS is applied (Performance governor), the second uses the Ondemand governor [START_REF] Pallipadi | The ondemand governor[END_REF]. The characterization method is not a real strategy. We have extracted the best configuration (number of GPP cores and their frequency) by testing all possible configurations offline (whereas the proposed solution selects it dynamically without such an effort). We also compared to the solution based only on PI controller [START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF] to show the impact of the classification we proposed. We finally compared the energy consumption with the one of the HDIP to evaluate how far is our solution from it.

Second, the accuracy of the model built in phase 1 is evaluated.

Table 2 The proposed video decoding energy consumption optimization summary.

Proposed solution

Classification + DVFS (PI controller)

State-of-the-art work

No DVFS (Performance Linux governor) [START_REF] Pallipadi | The ondemand governor[END_REF] DVFS (Ondemand Linux governor) [START_REF] Pallipadi | The ondemand governor[END_REF] Characterization proposed in [START_REF] Bey Ahmed Khernache | Hevc hardware vs software decoding: An objective energy consumption analysis and comparison[END_REF] DVFS (PI controller) [START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF] HW decoding (HDIP) of the target platform Table 3 The proposed solution energy saving (%) over state-of-the-art work.

State-of-theart work

Open Third, the stability of the output buffer, which is its occupation variation, is studied. We consider a system stable when the cores frequency does not change more than once in a second since this period is usually used to make group of pictures (GoP), e.g., for streaming applications.

Fourth, the overhead of the proposed solution (in percentage) is evaluated using the following formula: ratio_overhead = ps_time frame_dec_time * 100 [START_REF]Odroid-xu3 -odroid[END_REF] where ps_time represents the time spent to run the proposed solution (phases 2 and 3), and frame_dec_time represents the time required to decode a frame.

Results and discussion

In this section, the results of the HEVC SW decoding energy consumption optimization are described and analyzed.

Comparison to the state-of-the-art work

In case of Snapdragon 810 and Odroid-xu3 platforms, the proposed solution can save on average 40% and 30% of energy (mJ/Frame) compared to the Performance and Ondemand Linux governors, respectively. Then, the proposed solution not only determines dynamically the suitable GPP cluster and its clock frequency, in contrast to the characterization solution [START_REF] Bey Ahmed Khernache | Hevc hardware vs software decoding: An objective energy consumption analysis and comparison[END_REF], but also can save up to 7% of energy (mJ/Frame). The classification technique, phase 2, brings on average 20% of energy saving as compared to [START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF]. Finally, the ratio of energy between the proposed solution and the HW video decoding is about 3×.

PARMA-DITAM 2022

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Energy-Aware HEVC SW Decoding

To validate these results, experiments were conducted on RB3 platform which supports both HW and SW HEVC decoding. On this platform, the proposed solution can save up to 35%, 20%, 4%, and 23% of energy (mJ/Frame) compared to the Performance Linux governor, the Ondemand Linux governor, the characterization work, and PI controller solution, respectively. Concerning the HW video decoding, our solution consumes on average 4× more energy.

On all tested platforms, the miss-rate represents on average less than 5% of any given video sequence. In addition, our solution needs less than one second of decoding to enter the stable state of the output buffer size and thus the GPP frequency, as suggested by Jensen's inequality in [START_REF] Yao | A scheduling model for reduced cpu energy[END_REF].

Table 3 summarizes the energy saving percentage of the proposed solution over state-ofthe-art work.

Accuracy of the model

The model of frame complexity was trained with 70% of video frames dataset extracted from 33 videos sequences representing different durations and scenarios. The remaining 30% was used for validation. In case of Odroid-xu3 platform, the accuracy of the model was 93%, whereas 98% was achieved in case of RB3 platform. This indicates that at most 7 frames over 100 are not decoded by the right GPP cluster, e.g., they are decoded by the GPP LITTLE cluster instead of the big one. The result is that these frames may not be decoded within the deadline. This can be corrected by the PI controller of phase 3 (the miss rate was smaller then this proportion).

Stability of the output buffer

At the beginning of the decoding process, the GPP clusters are clocked at their highest supported frequency values. This allows to fill the output buffer as fast as possible to reach the desired output buffer size. Then, the display process starts receiving frames, and thus the PI controller starts monitoring the output buffer size.

The stability of this latter was reached in less than one second of decoding. The GPPs cores frequency changes at most once in a second of decoding. The fluctuation of output buffer size is due to the frames complexity which changes from one frame to another.

Overhead of the proposed solution

The overhead of the proposed solution is evaluated here. It is calculated using Equation [START_REF]Odroid-xu3 -odroid[END_REF]. The results show that the overhead represents on average less than 1% of the decoding time. That is, it is negligible compared to the gain of energy that the proposed solution permits to get.

Conclusions & future work

This paper presents a solution to reduce the energy consumption of HEVC decoding on a heterogeneous mobile platform. The proposed solution is split into three phases: (1) modeling of frame complexity, (2) assignment of frames to appropriate GPP cores using classification, and (3) frequency scaling using a PI controller with DVFS. Phases 2 and 3 solves RQ1 and RQ2, respectively. The established model in phase 1 is more than 90% accurate. This accuracy permits to exploit efficiently the heterogeneous character of mobile architectures, such as ARM big.LITTLE. Moreover, the classification has a great role to exploit the heterogeneity of ARM big.LITTLE architecture and limit the miss-rate. Actually, the proposed solution induces less than 5% of miss-rate, whereas 10% of miss-rate is observed when the Ondemand Linux governor is set up. In terms of the overhead, the proposed solution is very slight as it represents on average less than 1% of the frame decoding time. Finally, it should be noted that the HW video decoding presents the best trade-off between performance and energy consumption at the system level point of view.

In our future work, the aim is to apply our methodology to the successor of the HEVC standard, versatile video coding (VVC).

Figure 1

 1 Figure 1The proposed solution overview.

Figure 2

 2 Figure 2The proposed solution: phase 1 (Modeling of frame complexity).

Figure 3

 3 Figure 3The proposed solution: phase 2 (Assignment using classification).

Table 1

 1 Experimental setups.

		Snapdragon 810	Odroid-xu3	RB3
		[1]	[7]	[8]
			HW setup
	HEVC HW	Supported	Not supported	Supported
	HEVC SW	Not supported	Supported	Supported
	Power measurement		N6705A DC Power Analyzer [14]
			SW setup
	OS	Android 6.0 Linux kernel 3.10.84	Ubuntu 16.04 Linux kernel 4.14.176+	Linaro Linux 10.3 Linux kernel 5.4.0

PU (Prediction Unit) and CTU (Coding Tree Unit) are sub-parts of a tile. CTU is the basic processing unit of HEVC decoding process (conceptually corresponding to a Macro-block in prior standards)[START_REF] Ohm | Comparison of the coding efficiency of video coding standards-including high efficiency video coding (hevc)[END_REF].

All the aforementioned studies suffer from at least one of the following drawbacks. First, they do not take into account the multi-cores GPPs heterogeneity of mobile platforms. Second, they rely on detailed information to predict the complexity of a frame, e.g., number of bits contained in a CTU. However, these information are not available before to start decoding except if they are collected at the encoder side and are standardized. Third, they modify the decoding algorithm which makes it not complaint to the standard.In this paper, a solution based on parallelism and DVFS is proposed to save energy when performing video decoding. It is compliant to the HEVC standard. The proposed solution is composed of three phases: (1) modeling of frame complexity, (2) classification of video frames for an adaptive assignment, and (3) frequency scaling using feedback control with DVFS (as introduced in[START_REF] Lu | Reducing multimedia decode power using feedback control[END_REF]).

This work was supported by BPI France, Cap Digital, and Région Ile de France through the French project EFIGI.