domain: phys spi

text:
V Gupta

L Dilillo
email: dilillo@lirmm.fr

F Wrobel

A Zadeh

M Bernard
email: muriel.bernard@univ-montp2.f.r

G Tsiligiannis
email: tsiligiann@lirmm.fr

L Dusseau

-V. Gupta 1 PRESENTATION OF THE MTCUBE CUBESAT PROJECT

In this paper, we will present the MTCube project, with a focus on the preliminary considerations regarding the payload which will consist in flying different types of state-of-the-art COTS (Commercial Off the Self) memories. The objective of the mission is to assess their sensitivity with respect to SEEs (Single Event Effects) in the space environment in order to compare ground based tests results with in-flight data.

INTRODUCTION

The continuous technology development, improvement and miniaturization of electronic components have been advantageous to the development of spacecraft. However, the miniaturization of electronic components has led to a trend of increased sensitivity, especially considering Single Event Effects (SEE). Crucial devices in electronics are memories and it is of outmost importance to study how the new generation of memories behave and operate in space environment.

COTS (Commercial Off The Shelf) components, have already been considered for use by the space industry. They present the advantage of being at the same time cheap, low in mass and power consumption and easily purchasable compared to RadHard or military grade components. Nevertheless, as they are not designed carrying in mind their survivability in a space radiation environment, they have to be rigorously tested and characterized to ensure their reliability for space applications. While testing of memories can be performed in radiation facilities, they are not fully representative of the actual space environment, as it will be presented later. Besides, it is necessary to carry out in-flight technology demonstration of these memories in order to raise their TRL (Technology Readiness Level) for future applications in space missions.

The MTCube project, on which is proposed to fly the RES (Radiation Effect Study) Experiment, is a 1U CubeSat that aims at addressing the above. Its mission is to characterize and qualify state-of-the art commercial memories like SRAMs, FLASHs, and emerging technologies such as MRAMs and FRAMs for future space applications. MTCube is based on the Robusta platform that has been designed and built by students of UM2 (Université Montpellier 2), thanks to the experience gained on Robusta 1A and Robusta 1B projects [START_REF] Deneau | Robusta the firstborn French modular Cubesat[END_REF], [START_REF] Perez | Concepts for Modularity and Standardization of Electronic Boards on Small Satellites[END_REF].

COTS FOR SPACE APPLICATIONS

Since many years, electronic components used in space applications have been radiation hardened by design, by using schemes such as guard rings or triple well among others. Unfortunately, such hardening technology comes with a price: mass increases as well as power consumption and price.

In order to utilise the benefits of the emerging commercial technology like speed, low power consumption, lower mass and price, COTS components have been considered for use in space applications. This trend is closely linked to the emergence of small satellites, mainly built by Universities, such as the University of Surrey with the UoSAT serie, and thanks to improved capabilities in electronics allowing such a trend. Concerning the selection of COTS components, another important aspect, is the risk associated by the use of COTS: small satellites built by Universities are low risk missions unlike commercial or national space satellite projects. For some missions, radiation tolerance at the levels of COTS components may be considered sufficient.

As mentioned in the introduction, utilizing COTS components for space applications has been recently considered by national and international space agencies for the reasons mentioned above. In order to mitigate risks associated with the use of COTS components, several solutions may be implemented such as:

-Error Correction Codes (ECC) -Redundancy -Using several smaller components instead of one main component -Periodical restarts of electronic components -Optimizing the placement of components using other components to shield the most sensitive ones -A combination of all the above Depending on the space environment to which will be subjected electronic components, they may make use of the practices mentioned above. In order to evaluate the effects of radiation on the components that will be used for a specific space application, and consequently mitigate them, testing should be performed under similar conditions. Testing of components can be made in three different ways: simulation level testing, accelerated testing and real time testing. Space standards by ESA or NASA may be followed, such as [START_REF]Single Event Effects Test Methods and Guidelines[END_REF] or [START_REF]Test Procedures for the Measurement of Single-Event Effects in Semiconductor Devices from Heavy Ion Irradiation[END_REF] for SEE testing under heavy ion or proton irradiation.

One of the drawbacks of accelerated testing is its inability to be fully representative of the space environment: energies cannot reach the high energy levels found at space (always as a function of the environment the component is meant to operate) [START_REF] Ferlet-Cavrois | Influence of Beam Conditions and Energy for SEE Testing[END_REF], synergetic effects may be not tested due to time and/or cost constraints (dose and SEE); often due to limited beam diameter only parts of the integrated components can be set under test, and not the entire system; most of the time radiation testing is performed with the beam perpendicular to the surface of the component sometimes only making an angular dependency study [START_REF] Tipton | Device-Orientation Effects on Multiple-Bit Upset in 65 nm SRAMs[END_REF] whereas space radiation is often omnidirectional; due to time/cost constraint, radiation testing is performed at high flux that may not be representative of the space environment. Moreover, electrical degradation and temperature impact of the component after several years of mission in space environment is difficult to apprehend.

Hence, the possibility of testing components in a real space environment presents one of the best solutions that may be referred to as real-time testing. Results may provide valuable feedbacks to compare with accelerated testing results and simulation testing results, in order to evaluate how well they are representative of the actual environment. This may also allow the improvement of the guidelines on radiation testing to improve Radiation Hardness Assurance (RHA) methodologies. In this respect, small satellites and CubeSats in particular, present a very good advantage: they present a low cost, quickly available testbed which can be used to qualify space components in complement of accelerated testing in order to characterise their response to radiation for future space missions.

TYPES OF MEMORIES

Memories are used for different tasks depending on their performances. For example, memories are used as storage devices for boot software and other software, cache memory for microprocessors but also storage devices for telemetry/telecommand and scientific data. In this section, we will briefly review the types of memories that may be part of the MTCube payload and briefly see what are some of the SEE sources for each type of memory. SRAM (Static Random Access Memory) is a type of memory that has high performance in access time and it is volatile. As shown in Figure 1, the most common SRAM memory cell is based on 6 Transistors (6T) memory cell storing the bit, either '0' or '1', thanks to 4 MOS transistors connected in order to form two cross-coupled invertors (the two other transistors enable to access the memory cell) [START_REF] Baumann | Radiation-Induced Soft Errors in Advanced Semiconductor Technologies[END_REF]. The miniaturisation trend makes necessary to constantly test the sensitivity to radiation of new technology nodes, as new type of effects may appear. For example, direct proton ionization at low proton energy are new sources of error, due to increased sensitivity and high density memory cells [START_REF] Lawrence | Soft Error Sensitivities in 90 nm Bulk CMOS SRAMs[END_REF]. The number of MCUs (Multiple Cell Upsets) can considerably increase [START_REF] Cannon | Multi-Bit Upsets In 65nm SOI SRAMs[END_REF]. FLASH memories are non-volatile memories, often used as mass data storage or used to retain the boot software for example. FLASH memory cells are less sensitive to the radiation environment compared to SRAMs, but they may still be affected, not only at cell level but also in their control circuitry. Faulty control logic may produce large cluster of errors for example during the occurrence of SEFI (Single Event Functional Interrupt). [START_REF] Bagatin | Key Contributions to the Cross Section of NAND Flash Memories Irradiated With Heavy Ions[END_REF] provides a comprehensive list of potential effects of heavy ions radiation on FLASH memories.

The other types of non-volatile memories such as MRAM or FRAMs appear to be interesting candidates since they present high endurance capabilities with lower access time if compared to FLASH technology. The bit storage mechanism provides a good inherent radiation robustness of the memory cells according to literature such as in [START_REF] Nuns | Evaluation of Recent Technologies of Nonvolatile RAM[END_REF], while most of the potential errors may arise from the CMOS based peripheral circuits. As those memories are rather new on the market of commercial memories, characterising their behaviour is essential to assess their potential use on future spacecraft.

SPACE RADIATION ENVIRONMENT

The harsh space radiation environment has been and remains an important aspect to be considered during the design of satellites, in order to ensure their reliability for the duration of the mission. Other environmental aspects such as vacuum, thermal, atomic oxygen in upper atmosphere, micrometeorites, charging due to plasma of electrons and protons may also be considered but are out of the scope of this paper.

Radiation is a field of energetic of high speed particles or electromagnetic waves, that can be ionizing or non-ionizing. The Earth natural ionizing space radiation environment can be divided in three generic components [START_REF] Adams | Cosmic ray effects on microelectronics, Part 1: The near-earth particle environment[END_REF]:

-The trapped particles in the Earth's radiation belts (Van Allen belts) -The particles originating from the Sun: solar wind, Coronal Mass Ejections, solar flares -The Cosmic Galactic Rays (CGRs)

The Earth is surrounded by two radiation belts: the internal belt is mainly composed of protons and electrons whereas the external belt is mainly constituted of electrons. Alpha particles and heavy ions are also present but their proportion remains negligible. Figure 2 shows the extent of these radiation belts with respect to the Earth radius. The energy and fluxes of the particles in the Van Allen radiation belts depend on the altitude and inclination. Moreover, the belts are not static but rather dynamic as they are distorted by the solar wind, affected by the Sun cycles and the Earth's rotation [START_REF]Earth's radiation belt: Van Allen probes reveal 'zebra stripes' in space[END_REF].

The Sun contributes in 3 different ways to the radiation environment of the solar system: the solar wind, the solar flare and the CMEs (Coronal Mass Ejections). The solar wind is a constant flow of electrons and protons at supersonic speed coming from the Sun. The solar flares are a more periodical event modulated by the 11 year solar cycle. Solar flares release mostly energetic protons, but also alpha particles and heavy ions at nearly the speed of light along with electromagnetic radiation in all wavelengths. CMEs are larger events in which electromagnetic radiation as well as high energetic protons, alpha particles and heavy ions are released. High solar energetic particles can affect spacecrafts in the system solar but also spacecrafts protected by the Earth magnetic field especially spacecrafts with high inclinations and/or with higher altitudes.

Finally, CGRs originate from exploding stars in the Universe. They are considered omnidirectional and are constituted mainly of protons, alpha particles and heavy ions of very high energies in the order of GeV or higher some of which may penetrate the Earth radiation belts and affect any spacecraft in low earth orbit.

As mentioned in [START_REF] Barth | Member, Space, Atmospheric, and Terrestrial Radiation Environments[END_REF], the Space radiation environment is still being investigated and models are constantly being improved taking into account new advances in the scientific field in order to better understand their impact on constantly changing microelectronic technology.

RADIATION EFFECTS ON MEMORY DEVICES

The Space radiation environment, as presented in the previous section, affects microelectronics in two different ways via cumulative effects (Total Ionizing Dose, Displacement Damage) and Single Event Effects (SEEs). Effects of the particles mentioned in the previous section (protons, electrons and heavy ions) on semi-conductors are summarised in Figure 3.

Figure 3. Comparison between radiation sources and effects [START_REF] Ecoffet | Spacecraft Anomalies Associated with Radiation Effects[END_REF] The focus of the MTCube experiment as it will be presented below, are Single Event Effects mainly due to heavy ions and protons. SEEs are due to very highly energetic particles, such as protons and heavy ions from CGRs, SEP (Solar Energetic Particles) or even high energy protons from the Earth radiation belts. Those high energetic particles will pass through memory cells and matters, generating an ionized track of electron/hole pairs by two different mechanisms: direct ionization when the incoming particle generates itself electron/hole pairs; and indirect ionization when the incoming particle generates an ion recoil which will generate electron/hole pairs (Figure 4). If the ionized track is close enough to the sensitive region of a transistor (the drain), the created charges can be absorbed by the transistor and change its state (from OFF to ON). For an SRAM cell, this may induce a bit flip that can be classified as a Single Event Upset (SEU). Other types of SEE can be triggered by highly energetic particles, such as Multiple Cell Upsets (MCUs) or Single Event Latchups (SELs), amongst others.

MCUs are upsets occurring in a group of more than two adjacent cells as a result of the direct or indirect ionization of a single particle. MCUs are becoming a relevant phenomenon since the number of involved cells involved is increasing with the density of memories.

SEL are latch ups created by the activation of an inherent PNPN or NPNP parasitic thyristor in CMOS technologies, triggered by an ionizing particle, leading to a high current flow in the transistor which in turn may produce a short eventually leading to the failure of the component.

Figure 4. Basic mechanisms of radiation effects: generation of an ionizing track via direct or indirect ionization [START_REF] Ecoffet | Spacecraft Anomalies Associated with Radiation Effects[END_REF] As discussed in [START_REF] Schwank | Radiation Hardness Assurance Testing of Microelectronic Devices and Integrated Circuits: Radiation Environments, Physical Mechanisms, and Foundations for Hardness Assurance[END_REF], shielding of sensitive components may be done in the expense of increased mass. Shielding is only efficient in electron rich environment and not as much in proton rich environments. Besides, low energy nuclei form solar flares do not trigger a lot of SEE mechanisms. Hence, even if additional mass was not a penalty in the cost of a satellite (which is clearly not the case), shielding is not able to protect sensitive components from all radiation sources.

THE MTCUBE CUBESAT PROJECT 6.1 Background of the project

In Figure 5 the logos of the main actors of the MTCube project are presented. MTCube stands for Memory Test CubeSat. The "Centre Spatial Universitaire" (CSU) was created by the RADIAC research group, one of the world leading groups in the field of radiation effects on electronics and part of the IES research entity formed by the collaboration of the Université Montpellier 2 and CNRS (Centre National de la Recherche Scientifique). For more than 25 years, RADIAC has been providing support to industries and agencies in this field of radiation effects by investigating basic mechanisms and providing them with accelerated test methodologies. The CSU comes from the collaboration between the RADIAC group and the IUT of Nîmes. The CSU developed the Robusta platform which followed the CubeSat standard [START_REF]CubeSat Design Specification[END_REF].

The Van Allen foundation, aims at supporting nanosatellite activities in France, with corporate sponsorship of prestigious industrial partners such as Université Montpellier 2, Astrium, Intespace, 3D plus, ESA and CNES.

MTCube will fly different types of memories that may be used for future space applications. The selected memories will constitute the RES (Radiation Effect study by SEE) Experiment payload of MTCube which will be built under an ESA collaboration contract.

The main payload will be built by LIRMM, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, which is a research laboratory run in collaboration between Université Montpellier 2 and CNRS.

Objectives of the mission

The RES Experiment will consist in flying different types of memories in order to assess their sensitivity in the space environment thus providing data to be compared with accelerated testing and simulations.

The primary objective of MTCube is hence to develop, test and fly the RES Experiment and gather data for 2 years (nominal mission duration). During the development phase, a set of COTS memories will be selected for the experiment. The RES Experiment will use the Robusta 1U CubeSat platform [START_REF] Deneau | Robusta the firstborn French modular Cubesat[END_REF], [START_REF] Perez | Concepts for Modularity and Standardization of Electronic Boards on Small Satellites[END_REF].

Upon completion of the primary objective, the subsequent objectives will be:

to assess the in-orbit radiation sensitivity (SEE) of various state-of-the-art memory devices (SEU and SEL) to map the SEE response of these memories along the orbit to compare ground based RHA processes with in-flight radiation data to develop and provide a technology test bed for novel memory devices to measure the on-orbit radiation dose by the use of an in-house built dosimeter

The MTCube CubeSat will be developed, built and tested by the CSU as a student project implying a strong education aspect. Students from all level and several disciplines will be involved and trained under the lead of a team of professors. None of the Robusta platform subsystems are off the shelf equipment bought from CubeSat manufacturers.

Finally, during the full project, best practices as close as possible to the ECSS standards will be followed especially regarding Product Assurance (PA) but taking into account the specificities of a CubeSat project.

The Robusta CubeSat Platform

The MTCube CubeSat will be based on the 1U Robusta platform developed at the Université Montpellier 2 [START_REF] Deneau | Robusta the firstborn French modular Cubesat[END_REF], [START_REF] Perez | Concepts for Modularity and Standardization of Electronic Boards on Small Satellites[END_REF].

The 1U Robusta platform is built with the objective of being modular and allowing to "plug and play" the experiment board depending on the mission. Therefore the most fundamental structure of the Robusta platform consists of an in-house built 1U structure to which is mounted a motherboard.

The platform presents a common CAN Bus and Power BUS that are used by the other subsystems.

Hence, the major task of the project will be to design and build the payload, adapt the current Robusta platform to the needs of the payload, and perform environmental tests at system level in order to guarantee the good integrity of the satellite.

Two improvements between Robusta 1B and MTCube are related to the EPS (adding a microcontroller to manage intelligently the power distribution), and the Communication subsystems that are being upgraded. The Robusta 1B EPS and Communication subsystems will be implemented in MTCube.

Finally, the UM2 ground station will be the main ground station that will be used during the MTCube mission. Another secondary ground station will be the BMSTU ground station in Moscow (University of Bauman). It is possible that more ground stations may be used in collaboration with other universities but agreements are not confirmed at present.

MTCUBE RES EXPERIMENT PAYLOAD

Payload preliminary description

Besides the FLASH memory, we target to evaluate two recent SRAM technologies to compare the response of two technology nodes and architecture to space environment. The MRAM and FRAM are also proposed to compare the effects of a similar space radiation environment on those different commercial non-volatile memories. The final selection of the memories will be done when an exhaustive campaign of irradiation tests will be concluded.

Beyond the considered memories, the RES Experiment board will also comprise a dose monitoring system based on an OSL (Optically Stimulated Luminescence) dosimeter developed at by the RADIAC group. This OSL-NG is based on a previous OSL that flew on the ICARE instrument of the CARMEN-2 mission that flew onboard the JASON-2 CNES satellite [START_REF] Deneau | A compact dosimeter for space applications[END_REF].

The RES Experiment board will be designed to work fully autonomously during the nominal mission. The experiments will be run by a finite state machine (FSM implemented in a FLASH based FPGA which will be also responsible of the communications with the platform communication bus. The FPGA will include the Experiment controller, and a CAN controller. The FPGA will be coded in VHDL. The experiments will run concurrently but the experiment will adapt to the power supplied to the payload that may evolve during the satellite operation.

Besides the use of anti-latchup devices, in order to prevent any malfunctions due to the radiation environment, logic hardening methods will be used on the FPGA, as well as the CAN communication, experience and buffer controllers. The memory buffer will be hardened using a data reliability method by software. The buffer, that will store the error events detected on the memories prior to be sent to the main on-board computer subsystem, will have to be non-volatile to be able to store the data in the unlikely event of a power shutdown of the payload. Frequent resets of the FPGA will avoid any fault accumulations. A software watchdog will be added in the FPGA.

The following information will be gathered on orbit: data of the word that was corrupted along with the address, the timestamp and all additional information allowing to know during which test exactly the single events occurred. There will also be a record of the occurring latch up specifying the concerned devices as well as the timestamp of the event.

A simplified layout of the RES Experiment board is proposed on Figure 6. The FPGA will be directly connected to each memory while the FLASH memory will have a dedicated FPGA. The Voltage converter is not represented nor is the A/D converter will be used for the thermocouple measurements. Depending on space availability, it will be possible to integrate components on both sides of the PCB.

Figure 6: Layout of the proposed RES Experiment. The electrical circuitry is not represented besides the anti-latch up circuitry (the purple triangles) that will be used to count SEL on each memory.

Description of the tests performed

It is proposed that the memories shall be tested through two different types of tests. During static mode testing, a data pattern ('1's, '0's, or a combination or both) is stored in the memory cells. During the testing time, no operations are applied to the memory. After a defined time, the memory is read back and the stored data is compared with the initial one in order to detect any bit flips. On the contrary, as presented in [START_REF] Bosio | Advanced Test Methods for SRAMs -Effective Solutions for Dynamic Fault Detection in Nanoscaled Technologies[END_REF], dynamic testing stresses more the memory by successive read and write operations on the memory cells during the actual testing, increasing their sensitivity to radiation. For SRAMs, this operational mode is more realistic as the memory is often used as a cache memory requiring constant access to its data. One of the advantages of dynamic mode testing is its ability to assess the sensitivity of the peripheral circuitry which is constantly solicited during read/write operations, which is not the case in static mode testing. In [START_REF] Tsiligiannis | Evaluation of test algorithms stress effect on SRAMs under neutron radiation[END_REF], different dynamic patterns have been applied under neutron beam irradiation and the same patterns are being tested under heavy ion and proton beams [START_REF] Virtanen | Upgrades for the RADEF Facility[END_REF].

A scheduler will be responsible for a round robin scheme which will apply dynamic mode testing consecutively to all the memories while the others will be in static mode testing. This allows to obtain a homogeneous test time for the different devices and minimize the power consumption. The data will be processed by the FPGA and detected errors will be stored on a hardened memory buffer.

Selection of the tested memories

Initial radiation tests of memories have been initiated in order to better characterise the memories and select the most suitable commercial memories for the RES Experiment. Currently, two radiation tests have been made at the RADEF facilities at the University of Jyväskylä in Finland [START_REF] Virtanen | Upgrades for the RADEF Facility[END_REF], one with heavy ions and the other one with protons. The test setup and some results of heavy ions irradiation campaign are presented in Figure 8. During these tests, different types of SRAMs and a FRAM memory were tested under heavy ions [START_REF] Tsiligiannis | Efficient Dynamic Test Methods for COTS SRAMs Under Heavy Ion Irradiation, selected for poster presentation NSREC[END_REF]. Further tests will be performed to other types of memories in the next few months. Those tests will allow to extensively characterise the sensitivity of memories against SEEs. This will then allow obtaining the cross-section curve of the memories enabling to estimate the probability of a SEE for a particular type of energetic particle. This can then be extrapolated to the energetic radiation spectrum of the space environment to assess SEE probability for the mission. Inflight data will also provide feedbacks on those simulation tools in order to compare assumptions made by those simulation tools such as, for example, the sensitive volume depth.

Further steps

Some additional irradiation campaigns are planned to test all the potential memory candidates to heavy ion and protons at energy and flux levels representative of the space environment as well as to test the other devices that will compose the payload. Simulation will be performed to better understand the mechanism triggering SEEs. Thanks to the characterization of the memories by beam tests, an estimation of the number of SEUs will be performed through the tools OMERE, SPENVIS, MC-Oracle [START_REF] Wrobel | MC-ORACLE: A tool for predicting Soft Error Rate[END_REF] and other simulation tools.

The RES Experiment card will be designed and fully verified through functional and irradiation tests. In the meantime, improvements on the Robusta platform will be implemented, while the search for a suitable launcher will be made.

CONCLUSION

In this paper, we have briefly presented the space radiation environment and its effects on memories, focusing on SEEs. We have highlighted the crucial relevance of testing memories for future space applications. Then, we have introduced and detailed the MTCube project. The main goal of this project is to retrieve in-orbit data from state-of-the-art memories in order to assess their sensitivity under real space environment and compare with on-ground test results and simulations.

A major focus will put on SEE and those events that will be investigated during the nominal 2 year mission of MTCube. The low cost and short development time a CubeSat based platform will allow targeting such important objectives at academic level.

ACKNOWLEDGEMENT

This work was funded by Labex NUMEV.

Figure 1 .

 1 Figure 1. 6T memory cell diagram of an SRAM

Figure 2 .

 2 Figure 2. Altitude of the two Van Allen radiation belts. Internal belt mainly made of protons and electrons, external belt mainly made of electrons [13]

Figure 5 .

 5 Figure 5. The main actors of the MTCube project along with the logo of the project

Figure 8 :

 8 Figure 8: (left) test set-up for radiation testing, (right) cross section curve of the SRAM 65 nm under Heavy Ion irradiation.


Madeleine Abernot

Thierry Gil

Aida Todri-Sanial

ONN-Based On-chip Learning for Obstacle Avoidance on Mobile Robot

The amount of data available to process at the edge has increased drastically in the last decades with the emergence of edge devices in numerous domains. Moreover, the increasing amount of data requires powerful artificial intelligence (AI) algorithms, such as deep learning algorithms to process the data, and they are very power-hungry. Thus, there is a lot of ongoing research on how to train and compute on edge devices. Current hardware architectures based on von Neumann are not adapted for AI algorithms due to the processing to memory transfer bottleneck [1]. Recently, neuromorphic computing, which proposes brain-inspired hardware-based paradigms, has emerged as a suitable solution to edge computing, removing the von Neumann bottleneck [1]. Oscillatory Neural Network (ONN) is a promising neuromorphic computing paradigm for AI at the edge. ONNs [2,3,4] are networks of coupled oscillators using the natural synchronization behavior of oscillators to compute. Information is encoded in the phase relationship among oscillators to reduce the voltage amplitude and limit power consumption [5]. In state-of-the-art, ONN is configured with a fullyconnected recurrent architecture, and trained with unsupervised learning to solve auto-associative memory tasks, like pattern recognition. In the framework of the EU H2020 NEURONN [START_REF] Todri-Sanial | NEURONN[END_REF] project, we explore ONN architectures, learning algorithms, and applications to showcase ONN advantages for edge computing. We developed a fully-connected recurrent ONN in digital to implement it on FPGA to explore ONN edge applications and on-chip learning capabilities [START_REF] Abernot | [END_REF]. For example, using the ONN autoassociative memory properties, we demonstrated it can efficiently solve real-time obstacle avoidance application on mobile robots equipped with proximity sensors [START_REF] Abernot | IJCNN[END_REF][START_REF] Abernot | [END_REF]. In this case, we cascade two ONNs, one trained to detect obstacles and the second trained to define the novel robot direction. Later, we updated the system with an all-in-one architecture based on a Xilinx Zynq processor to have an ONNbased System-on-Chip (SoC) capable of reading, conditioning, and treating sensory data [START_REF] Abernot | [END_REF]. In parallel, we also explore ONN on-chip learning capabilities with the digital design. Recently, we proposed a novel architecture, also based on the Zynq processor, to allow ONN on-chip learning for autoassociative memory tasks [10]. In this work, we combine both the obstacle avoidance application with the on-chip learning capability. We propose an architecture that incorporates the on-chip learning in the ONN-based SoC architecture configured for robot obstacle avoidance, see Fig. 1. In particular, we propose to have a pre-trained ONN to detect obstacles, and a non-trained or half-trained ONN to define direction. The training of the second ONN is done by using an additional post-processing from the first ONN output to define a direction. This computed direction is then compared with the direction given by the second ONN output. A difference between the two activates the training of a novel pattern. After numerous cycles without any difference, we consider the second ONN trained, and we remove the post-processing algorithm. Results from [START_REF] Abernot | [END_REF] and [10] are encouraging to perform real-time on-chip learning with ONN for mobile robot obstacle avoidance application.

Fig. 1 :

 1 Fig. 1: Schematic of the architecture to perform ONN on-chip learning for obstacle avoidance.

Acknowledgment

This work was supported by the European Union's Horizon 2020 research and innovation program, EU H2020 NEURONN (www.neuronn.eu) project under Grant 871501. We thank CNRS, CSIC, and AIM collaborators for the fruitful exchanges with members of the project.


Madeleine Abernot
email: madeleine.abernot@lirmm.fr

Aida Todri-Sanial

Oscillatory Neural Networks Implemented on FPGA for Edge Computing Applications

Keywords: Oscillatory neural networks, Edge AI, Pattern Recognition, Image edge detection, Robot obstacle avoidance

This PhD work focuses on Oscillatory Neural Network (ONN) computing paradigm for edge artificial intelligence applications. In particular, it uses a digital ONN design implemented on FPGA to explore novel ONN architectures, learning algorithms, and applications. First, using a fully-connected ONN architecture, ONN can perform pattern recognition, applied in this work for various edge applications, like image processing and robotics. Then, this work introduces layered ONN architectures for classification tasks applied to image edge detection.

I. INTRODUCTION

In recent years, neuromorphic computing has emerged as a promising hardware for edge artificial intelligence implementation. In particular, Oscillatory Neural Network (ONN) [START_REF] Csaba | Coupled oscillators for computing: A review and perspective[END_REF], [START_REF] Todri-Sanial | How Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase[END_REF] emerges as a suitable solution for edge computing [START_REF] Delacour | Oscillatory neural networks for edge ai computing[END_REF]. ONNs are artificial neural networks where each neuron is an oscillator coupled with synaptic elements. ONN phase computing, with information represented in phases between oscillators, allows to limit voltage amplitude and reduce power consumption [START_REF] Delacour | Energy-Performance Assessment of Oscillatory Neural Networks based on VO2 Devices for Future Edge AI Computing[END_REF]. This PhD work explores novel ONN architectures, learning algorithms, and applications with a digital implementation on FPGA [START_REF] Abernot | Digital Implementation of Oscillatory Neural Network for Image Recognition Applications[END_REF]. In state-of-the-art, ONN is used with a fullyconnected architecture to solve auto-associative memory tasks. This work showcases ONN auto-associative memory properties for edge applications using ONN for real-time image recognition and obstacle avoidance on mobile robots. Additionally, other architectures are investigated to perform beyond autoassociative memory tasks. Two-layer ONN architectures are introduced with bidirectional or feedforward connections to solve hetero-associative memory or classification tasks. The two-layer ONN architectures are applied for image edge detection applications.

II. ONN COMPUTING PARADIGM

ONN is a neuromorphic paradigm inspired by brain oscillations. ONNs are networks of coupled oscillators, where each neuron is an oscillator coupled by analog or digital synaptic elements. ONN uses phase computing, where information is represented in the phase difference between oscillators. ONN This work was supported by the European Union's Horizon 2020 research and innovation program, EU H2020 NEURONN (www.neuronn.eu) project under Grant 871501. inference starts by initializing oscillators' phases. Then, phases evolve depending on coupling elements until phase stabilization to a stable point representing the output. The inference is associated with the minimization of an intrinsic energy parameter. Coupling elements define the energy landscape, which allows associating the correct output given an input. Coupling elements are configured during learning to solve specific tasks efficiently.

III. ONN DIGITAL DESIGN

This work focuses on a specific ONN implementation with a digital ONN design on FPGA [START_REF] Abernot | Digital Implementation of Oscillatory Neural Network for Image Recognition Applications[END_REF]. In this design, each oscillator is a 16-stage digital oscillator. It can represent 16 phases between 0 o and 360 o , oscillating between 0 and 1. Synapses are 5-bits signed registers allowing integer synaptic weight amplitude between [-15; +15].

IV. FULLY-CONNECTED ONN FOR PATTERN RECOGNITION

ONNs with fully-connected architecture can perform autoassociative memory tasks. It can learn patterns and retrieve them from corrupted information, executing pattern recognition. Learned patterns are memorized in the synaptic couplings between oscillators. Synaptic weights are commonly computed from unsupervised learning rules and normalized to fit weights amplitude from implementation. ONN for pattern recognition can be used for various edge applications. For example, in this PhD work, ONN is used to perform real-time digits recognition from a camera stream [START_REF] Abernot | Digital Implementation of Oscillatory Neural Network for Image Recognition Applications[END_REF], see Fig. 1.a). The ONN learns five 10x6 images representing digits (0 to 4) and can retrieve them from input images with flipped pixels. At first, learning is computed offline, but more recently, the digital ONN architecture has been modified to allow on-chip learning [START_REF] Abernot | On-Chip Learning with a 15-neuron Digital Oscillatory Neural Network Implemented on ZYNQ Processor[END_REF]. In this case, a 5x3 ONN is trained on-chip with up to 3 digits (0 to 2).

Additionally, a second robotic application is developed with ONN configured for pattern recognition to perform obstacle avoidance. Two cascaded-ONNs are included in robots equipped with proximity sensors. Proximity values are encoded to become inputs of the first ONN. The first ONN detects obstacles, and the second ONN defines a novel direction to avoid detected obstacles. A first version is tested on the industrial robot E4 [START_REF] Abernot | Oscillatory Neural Networks for Obstacle Avoidance on Mobile Surveillance Robot E4[END_REF] and shows efficient performances to avoid obstacles and hollows in real-time (Video). Then, an allin-one architecture is developed to have an ONN-based Systemon-Chip (SoC) capable of reading, conditioning, and treating sensory data based on ONN [START_REF] Abernot | Oscillatory Neural Network for Edge Computing: A Mobile Robot Obstacle Avoidance Application[END_REF]. The system is tested on an Arduino robot equipped with 8 proximity sensors, see Fig. 1.b).

V. LAYERED ONN FOR CLASSIFICATION

The latest research focuses on ONN layered architectures to enlarge the scope of applications. First, a bidirectional twolayer architecture is introduced to perform hetero-associative memory by associating data with different dimensions. Using an adapted Hebbian rule with learning patterns from stateof-the-art Sobel filters, this novel architecture can replace convolutional filters to perform image edge detection [START_REF] Abernot | Oscillatory Neural Network as Hetero-Associative Memory for Image Edge Detection[END_REF], see Fig. 2. However, some edges are still incorrectly retrieved. Additionally, the main limitation is the latency of the computation. Using parallel ONNs can partially solve the problem, but for large images, resource utilization limits the number of parallel ONNs, increasing latency. Thus, an alternative architecture is introduced with the digital ONN to solve latency limitations and edge detection precision. Using a two-layer feedforward ONN requires only a 'one-shot' computation and less weighted sum computation, thus computing faster with fewer resources. The feedforward ONN is introduced in [START_REF] Abernot | Two-Layer Oscillatory Neural Networks for Image Edge Detection: Study of Bidirectional and Feedforward Architectures[END_REF] and applied to image edge detection by customizing weights from bidirectional twolayer ONN, see Fig. 2. The feedforward ONN shows better precision, resource utilization, and latency on the image edge detection task than the bidirectional ONN [START_REF] Abernot | Two-Layer Oscillatory Neural Networks for Image Edge Detection: Study of Bidirectional and Feedforward Architectures[END_REF].

VI. CONCLUSION

This PhD work explored various ONN architectures and learning algorithms to enlarge the scope of ONN applications for artificial intelligence at the edge. In particular, the ONN with fully-connected architecture was used to perform pattern recognition for image processing and robotic applications. Additionally, a solution was proposed to achieve learning on-chip for pattern recognition. Then, this work introduced layered-ONN architectures which showed efficient performances to solve the image edge detection application.

Fig. 1 .

 1 Fig. 1. a) Fully-connected ONN applied to real-time digits recognition from camera stream. b) ONN-based SoC to read, condition, and treat sensory data.

Fig. 2 .

 2 Fig. 2. Two-layer ONN architectures applied to image edge detection.

ACKNOWLEDGMENT

We thank T. Gil (LIRMM, University of Montpellier, CNRS) for helping in developping ONN application demonstrators. We thank M. Jimenez, Dr. M. J. Avedillo, Dr. J. Nunez, and Dr. B. Linares-Barranco (CSIC-IMSE, University of Sevilla) for providing a first digital ONN design and for useful discussions about ONN. We thank E. Kurylin, T. Hardelin, A. Magueresse, S. Gauthier, and Dr. T. Gonos (A.I.Mergence, Paris) for their contribution to integrate ONN in their industrial robot E4.