domain: shs sdv

text:
Patrice Autfray

François Bélières

Jean- François Belleres

Structure, practices including biodiversity, performance (income)

Keywords: Agroeocology, Biodiversity, Transition, Agroforestery, pest and disease, Ecosystem services, Efficiency, trade-offs and synergies, modelling, multistakeholders platforms

System approach

List of the datasets produced by the project

Standards and metadata

Metadata standards No Data format Excel File management

No naming standard or classification. File management is unique to each researcher

Responsibilities, intellectual property rights

Person in charge of the data collection Ollo Sib

Person in charge of the data analysis Ollo Sib, Eric Vall

Intellectual property rights

Governed by the CIRDES-CIRAD convention.

Aspects éthiques

No sensitive data

Data sharing, diffusion and reuse

During the project Acess limited to Burkina Faso team After the project Acess limited to Burkina Faso team

Standards and metadata

Metadata description

No

Data format Excel File management

No naming standard or classification. File management is unique to each researcher

Responsibilities, intellectual property rights

Person in charge of the data collection Béatrice Rhino

Person in charge of the data analysis Béatrice Rhino

Intellectual property rights Cirad

Aspects éthiques None

Data sharing, diffusion and reuse

During the project Shared with project partners After the project Dataverse CIRAD Type of license CC BY-NC-ND 4.0

Data repository

Dataverse CIRAD

Madagascar -Agronomical Experimental datasets produced Dataset(s) description

	Dataset reference number M1 M2 M3 M5 BF1 BF2 BF3 CR1 N1 DMP for dataset(s) Pays Madagascar Madagascar Madagascar Madagascar Burkina Faso Burkina Faso Burkina Faso Costa rica Nicaragua N4 Nicaragua Data sharing, diffusion and reuse Dataset name Stradiv 1 Experimental Design Stradiv 2 Experimental Design Monitoring of reference farms MO 2015-2019 Stradiv 4 Prototyping cropping systems 2017-2019 Survey of dairy farms Monthly monitoring of dairy cows' workshops Agronomic monitoring of fodder banks the production and presence of whiteflies Sarah Capeli Ecosystem services and coffee production la Dalia Follow-up co-design UCA LAI : leaf area index Data collected on the STRADIV 1 experimental design (96 Description * 9 * 5.1) diversified rotation and effect on performance of cropping systems in the Vak in Ivory. Agronomic data, plot itk, plant material, test plan, early soil analysis, manure data, weed biomass data, flowering rice LAI, soil faunas (monoliths, pit fall trap), SPAD data (Rice, peanut, sorghum), yield component rice, biomass (rice, groundnut, sorghum, mucuna, nut, C. spectabilis, cowpea) At present data for 2015-2016. Data sets related to WP2 and WP3. Data collected on the STRADIV 2 device (60 * 9 * 15m) in the Vak in Ivory. + 17 farmers plots agronomic data, Plot itk, plant material, test plan, manure data, weed biomass data, soil faunas (monoliths, pit fall trap), data on white grubs (species, estimated yield (Rice, groundnut, sorghum), yield component in rice, biomass (rice, pen) Weed surveys ° + recoveries For now data 2015-2016. 20 farms : multi-year structure, , resource map flows, livestock feedings, incomes and labor time for rainfed and lowland crops, local practices and innovations performance Data collected on the STRADIV 4 device, 2 fields (rice and rotation) * 144 * 39m -2 ) in the Vak in Ivory. + 20 farmers involved in assessment, multicriteria agronomic data, including weed biomass, striga counting, white grubs counting, yields of rice and maize, cassava, groundnut, bambara nut, cowpea, soybean), yield component in rice, crop and legume cover crop biomass, farmers scoring data assessment 20 farms Monthly monitoring of 15 farms: zootechnic data, milk production, cow weight measurements, what they eat, farrowing (18 months). Agronomic monitoring of fodder banks: data on growth measurements of perennial plants (height, diameter, number of leaves of shrubs), monitoring on the production of leaves with cuts, soil analysis. Evaluation of cover crops 20 farms: topoclimatic conditions, coffee plant physiology and yield (age, destitutes, size, practices), characteristics of cover plants: litter, % soil cover. Species "native" or selected by producers. Botanical description of native species. Inputs (pesticides), practices (method of weed control, number of passages), working time, and cost of production. Area of Turialba. Over 6 months, 1 pass per farm. Measurement of light transmissive to the ground (densiometer). Network of 60 plots on 3 farms, management intensity gradient: Conventional -organic. Management at the and services: impact of diseases and pests. Measure 4 / year incidences and severities foliar diseases. Link with soil fertility (soil analysis on a subsample, ammonium nitrate mineralization), + bait lamina data. Stock measurements litter in relation to moisture. Services: carbon stock in aerial biomass, water ecosystem service, soil fertility, habitat (biodiversity index). Regulation of the bark beetle through predation by insects (ants) (to be done). Follow-up of production and diseases over 2 years, the remainder 1 year. Data on about 15 producers over 2 year, to evaluate the interaction between 2 shade levels and 3 fertilization SPAD : instrument d'analyse de la teneur en chlorophylle rendement de cultures (riz, arachide et sorgho) : composante de rendement Forma t Excel Excel files for proces sing (SPSS XLSTAT ) Excel Excel Excel Excel Excel Excel During the project Data sharing on request with project partners After the project So far, data sharing hasn't been scheduled Type of license So far, no license defined Data repository Dataverse CIRAD Data identifier DOI CIRAD Dissemination date Pas de mise à disposition pour l'instant Data reading Words, Excel, Paint, PostgreSQL Sensitive data No sensitive Data. M1, M2 Dataset description et Biomasse treatments (simple, double and used of efficient Biomasses d'adventices microorganisms). Characterization of shade (percentage, Biomasses de plantes de service : Mucuna, C. spectabilis, niébé species and number of trees by strate (10-4m;4-Nombre et espèces d'individus de la faune du sol 10m;<10m), impact on foliar pest and deases, Nature of the dataset Experimental or observational data. production. Excel Method of production Experimental agronomic design Economic analysis of each options to evaluate cost and benefits. Work on Excel file (Aude Ripoche) Recommended lifetime Tools for data processing Backup under a PostgreSQL database (Sandrine Auzoux) Final volume of archived data <100Mo Biophysical and economic analysis made by a local Estimated volume of data <100Mo Data to be destroyed No data to destroy technician. Research question and experimental design was establish with the producers. Analysis of data and Existing publications related to this dataset No publication at the moment Name of person in charge of archiving and preservation None identification of the more sustainable option performed Associated costs None during participative workshop. farm level (technical itinerary, plot management). 3 Biophysical variables, harvested between 2015 and Micros oft Access and QGIS BF5 Burkina Faso network of local stakeholders involved in fodder species present on the plots. Temperature and humidity (0.15 and 15-30 cm). Thèse A Nijmeijer (autre projet : fodder production Excel Distribution of the canopy on the ground, name of the architecture (typology), soil physico-chemical analysis Analysis of the networks of stakeholders involved in Camille protocol (distance, angles), height, diameter, basal area. measurements of functional features (tree leaves), cocoa Excel, Analysis of the N2 Nicaragua plots on 3 farms, shade species: Mapping of distribution of shade trees Excel C1 Cameroun diversity and species specific richness, Cocoa yields, Excel BF4 Burkina Faso Agronomic monitoring of fodder plots Agronomic monitoring of fodder plots (itk, yields, Network of 60 according to the holdings. Structure and composition of per variable): biomasses of several compartments, environment) : corn associated with service plants Excel species of trees of common shade. 5 repetitions. The type of shade on each holding + other species which vary 2018, on 42 samples, 1 sample = a plot of 800 m2 cocoa agroforestry system, single catch data (1 data per plot DMP for dataset(s)	Data storage and access Database PostGreSQL, Access on request restricted to project partners Database PostGreSQL, Access on request restricted on project partners Database PostGreSQL, Access on request restricted on project partners Access on request restricted on project partners Access on request restricted on project partners Access on request restricted on project Martinique Access on request restricted on project partners Access on request restricted on project partners Access on request restricted on project partners, UCA, CATIE, union of cooperatives. partners partners restricted on project Access on request partners (WUR ?) Access on request restricted on DP restricted on project Open data SPAD partners Access on request Access on request restricted on project	Dataset leader Aude Ripoche, CIRAD Aude Ripoche, CIRAD Patrice Autfray, CIRAD Ollo Sib (PHD) Ollo Sib (PHD) Ollo Sib (PHD) CIRAD Karel van den meersch e Martin Notaro (thésard) Clémenti ne Allinne Cirdes Dabiré, Der CIRAD Saj, CIRAD Stéphane Patrice Kouakou,	Related WP(s) WP 2, WP 3 WP 2, WP 3 WP4 WP4 WP4 WP4 WP 2 WP 4 WP 4 WP1 WP2 WP4
			crops	in each parcel + weather station. 4 coffee plants SAFSE)
	A1	Martinique	Effect of tomato-related cover on	Experimental results on Tomato associated with service plants: abundance of pests and auxiliaries, yield… identified (indicators of potential product and losses). Measure between the relationship between diversity	Excel	Access on request restricted on PO	Béatrice Rhino,	WP2
		Intellectual property rights	Governed by the FOFIFA-CIRAD convention.
		Aspects éthiques		No personal data Collected data on IVORY experimental design site and in networks
				farms in partnership with Madagascar

Measurement in 2 steps. SE produced in plots of coffee. First survey of one hundred plots: soil analysis, tree inventory (number, size, species), aerial tree biomass, coffee yield surveys, water quality indicator according to the number of phyto treatments. To explain the factors of variation in services. Then more detailed follow-up surveys on 27 very different plots in terms of agricultural production and services: what are the determinants of service provision? Over 1 year, the Dalia.

Re-use of existing data

No existing data reused

Intended future uses

Possibility of reuse by researchers for closed research questions to compare data and results

Users of data

Scientific community

Standards and metadata

Metadata standards Crop ontology, Darwin core, EML Data format Format Excel, jpg, txt File management No naming standard or classification. File management is unique to each researcher Responsibilities, intellectual property rights Person in charge of the data collection Leader : Aude Ripoche, UR Aïda/CIRAD other contributors : Richard Randriamantsoa, Rasambatra Elias Romélio, Patrice Autfray, Eric blanchart, jean trap, Djibril Djigal Person in charge of the data analysis Aude Ripoche, CIRAD

Archiving and preservation (including storage and backup)

Storage and backup during the project

The datasets are saved on an internal CIRAD server (managed by Sandrine Auzoux), and the data leader keep the original datasets on their laptop preservation, localisation So far, data preservation hasn't been scheduled

M3 Madagascar -Monitoring of reference farms MO 2015-2018 Dataset(s) description

	Standards and metadata
	Metadata standards	DDI
	Data format	Format Excel, jpg, txt
	File management	No naming standard or classification. File management is unique to each researcher
	Responsibilities, intellectual property rights
	Person in charge of the data collection	Patrice Autfray, CIRAD Hery Zo Rakotofiringa, FOFIFA
	Person in charge of the data analysis	Patrice Autfray, Jean-François Bélières CIRAD, Hery Zo Rakotofiringa, FOFIFA
	Intellectual property rights	Accord Fofifa Cirad
	Ethical aspects		No specific protocol to manage personal data
	Data sharing, diffusion and reuse
	During the project	Access on request restricted on DP SPAD
	After the project	Access on request
	Type of license	No licence
	Data repository	Dataverse Cirad (in the near future)
	Data identifier	DOI Cirad
	Dissemination date	None
	Data reading	Excel
	Sensitive data	personal data about farmers
	Dataset description		20 farms : Structure, performance practices, farm and plot levels, pluri-annual recording
	Nature of the dataset		observation and survey dataset
	Method of production		survey protocol
	Tools for data processing		Microsoft Access and export files for processing (SPSS XLSTAT)
	Estimated volume of data		<100Mo
			Naudin K., Autfray P., Dusserre J., Penot E., Raboin L.M., Raharison T.,
			Rakotoarisoa J., Ramanantsoanirina A., Randrianjafizanaka M.T, Rasolofo L.I.,
	Existing publications related to this dataset	Raveloson N., Razafimahatratra M., Salgado P., Sester P., Vom Brocke K., Scopel E., 2018. L'agro-écologie à Madagascar : de la plante au paysage. Dans: Côte F.-X., Poirier-Magona E., Perret S., Rapidel B., Roudier P., Thirion
			M.-C. (eds), La Transition agro-écologique des agricultures du Sud,
			Agricultures et défis du monde, AFD, Cirad, Éditions Quae, Versailles.
	Re-use of existing data		Access request restricted on DP SPAD
	Intended future uses		Possibility of reuse by researchers for closed research questions to compare data and results
	Users of data		Scientific community

Archiving and preservation (including storage and backup)

	M4 Madagascar -
	Storage and backup during the project	Personal computer
	preservation, localisation	Dataverse Cirad (in the near future)
	Recommended lifetime	10 ans
	Final volume of archived data	< 100 Mo
	Data to be destroyed	No
	Name of person in charge of archiving and preservation	Dataverse administrator

Investigation diagnosis of farms MO 2014-2015 Dataset(s) description

  Razafimahatratra H. M., 2018. Agriculture de conservation et moyens d'existence des exploitations agricoles. Cas du Moyen Ouest de Madagascar. SupAgro Montpellier, ED Économie et Gestion de Montpellier. Raharison T., Bélières J.-F., Salgado P., Autfray P., Razafimahatratra H. M. et Rakotofiringa H. Z., 2017. Agro-biodiversité dans les exploitations agricoles familiales du Moyen Ouest de Vakinankaratra : des paysans en avance sur la recherche et le développement agricole durable ? Forum de la recherche sur la biodiversité et le développement durable -Fianarantsoa/Madagascar, 29-30 Novembre 2017. 14p. Razafimahatratra H. M., Raharison T., Bélières J.-F., Autfray P., Salgado P. et Rakotofiringa H. Z., 2017. Systèmes de production, pratiques, performances et moyens d'existence des exploitations agricoles du Moyen-Ouest du Vakinankaratra. SPAD CIRAD/FOFIFA/GSDM. Antsirabe (Madagascar) 103 p. http://agritrop.cirad.fr/586881/ Sourisseau J.-M., Bélières J.-F., Marzin J., Salgado P. et Maraux F., 2018. Les moteurs du développement de l'agro-écologie en Afrique subsaharienne : illustration sur les Hautes Terres malgaches. In La transition agro-écologique des agricultures du Sud. Côte, F.-X., Poirier-Magona, E., Perret, S., Roudier, P., Rapidel, B. et Thirion, M.-C. Ed., Versailles, AFD, Cirad, Éditions Quae, pp. 183-202.

	Standards and metadata
	Metadata standards	DDI
	Data format	Format ACCESS, SPSS, Excel
	File management	No naming standard or classification. File management is unique to each researcher
	Dataset description Responsibilities, intellectual property rights Survey dataset
	240 farms representatives of all farms of 8 fokontany in 4 communes (zones Person in charge of the data Jean-François Bélières, CIRAD
	collection		intervention BVPI SE/HP project), which is 4 379 farms : structure (land, Tahina Raharison (GSDM/SupAgro),
			livestock, people, agricultural equipment, other equipment, buildings, etc.), Hanitriniaina Mamy Razafimahatratra (FOFIFA),
	practices and performances on all parcels (2 158 parcels), products and Person in charge of the data analysis Jean-François Bélières, CIRAD
			expenses, gross margin and net margin for all activities (including livestock Tahina Raharison (GSDM/SupAgro),
			farming , activities off farm) , , agrobiodiversity for crops and livestock, Hanitriniaina Mamy Razafimahatratra (FOFIFA),
	Intellectual property rights	performance (incomes) far all activities (one year : 2014/15) Accord Fofifa Cirad
	Nature of the dataset Method of production Aspects éthiques		Survey dataset (2 visits to households) Survey protocol (2 visits to households) No specific protocol to manage personal data
	Tools for data processing		Microsoft Access and export files for processing (SPSS XLSTAT)
	Estimated volume of data		<100Mo
	Existing publications related to this dataset Data sharing, diffusion and reuse
	During the project	Access on request restricted on DP SPAD
	After the project	Access on request
	Type of license	No licence
	Data repository	Dataverse Cirad (in the near future)
	Data identifier	DOI Cirad
	Dissemination date	None
	Data reading	Excel
	Sensitive data	personal data about farmers
			Razafimahatratra Hanitriniaina Mamy, Bignebat Céline, Bélières Jean-François, David-
			Benz Hélène, Penot Eric. 2017.Livelihood assets as determinants of adoption and
			disadoption of conservation agriculture in Western Madagascar. 1 poster EAAE
			Congress -Towards Sustainable Agri-food Systems: Balancing Between Markets and
			Society. 15, Parme, Italie, 29 Août 2017/1 Septembre 2017
	Re-use of existing data		Access request restricted on DP SPAD but wider use possible
	Intended future uses		Possibility of reuse by researchers for closed research questions to compare
			data and results
			Possible use by development to analyse the evolution of the farming system.
	Users of data		Scientific community. But also rural development expert

Archiving and preservation (including storage and backup)

	M5 Madagascar -
	Storage and backup during the	Personal computer
	project
	preservation, localisation	Dataverse Cirad (in the near future)
	Recommended lifetime	10 ans
	Final volume of archived data	< 100 Mo
	Data to be destroyed	No
	Name of person in charge of	Dataverse Administrator ?
	archiving and preservation

Stradiv 4 Prototyping cropping systems 2017- 2019 produced Dataset(s) description

	Data sharing, diffusion and reuse
	During the project	Data sharing on request with project partners
	After the project	So far, data sharing hasn't been scheduled
	Type of license BF 1	So far, no license defined
	Data repository	Dataverse CIRAD
	Data identifier	DOI CIRAD
	Dissemination date	Pas de mise à disposition pour l'instant
	Dataset description Data reading Sensitive data	Words, Excel, Paint, PostgreSQL Multicriteria agronomic data, flowering date, chlorophyll content indicator, weed biomass, striga and white grubs counting, yields of rice and maize, No sensitive Data.
			cassava, groundnut, bambara nut, cowpea, soybean, yield component in
	rice, crop and legume cover crop biomass C and N inputs Archiving and preservation (including storage and backup)
	Storage and backup during the	Farmers scoring data assessment The datasets are saved on an internal CIRAD server (managed by Sandrine
	project		Auzoux), and the data leader keep the original datasets on their laptop
	Nature of the dataset preservation, localisation Method of production Recommended lifetime Tools for data processing Final volume of archived data Data to be destroyed Estimated volume of data Name of person in charge of Existing publications related to this dataset archiving and preservation Associated costs	So far, data preservation hasn't been scheduled Experimental or observational data. Experimental agronomic design <100Mo Work on Excel file (Patrice Autfray) No data to destroy Backup under a PostgreSQL database (Sandrine Auzoux) None <100Mo No publication at the moment None
	Re-use of existing data		No existing data reused
	Intended future uses		Possibility of reuse by researchers for closed research questions to compare
			data and results
	Users of data		Scientific community
	Standards and metadata
	Metadata standards	Crop ontology, Darwin core, EML
	Data format	Format Excel, jpg, txt
	File management	No naming standard or classification. File management is unique to each researcher
	Responsibilities, intellectual property rights
	Person in charge of the data	Leader : Patrice Autfray, CIRAD
	collection		other contributors : Richard Randriamantsoa, FOFIFA
	Person in charge of the data analysis	Patrice Autfray, Philippe Letourmy CIRAD
	Intellectual property rights	Governed by the FOFIFA-CIRAD convention.
	Aspects éthiques		No personal data
			Collected data on IVORY experimental design site and in networks
			farms in partnership with Madagascar

Burkina Faso -Survey of dairy farms Dataset(s) description

	Dataset description		Survey of dairy farms 20 farms in 2015 in Koumbia , Nasso, Bobo-Dioulasso
	Nature of the dataset		Survey
	Method of production		Questioning from Ollo Sib to farmers
	Tools for data processing		Excell
	Estimated volume of data		245 KO
			Sib O, Bougouma-Yameogo V M C, Blanchard M., Garcia E., Vall E., 2017,
	Existing publications related to	Production laitière à l'Ouest du Burkina faso dans un contexte d'émergence
	this dataset		de laiteries diversifié des pratiques d'élevage et propositions d'amélioration.
			Rev. Elev. Med. Vet. Pays Trop., 70 (3):81-91, doi:10,19182/remvt,3152
	Re-use of existing data		No
	Intended future uses		No
	Users of data		Ollo Sib (PHD) Eric Vall
	Standards and metadata
	Metadata standards	No
	Data format	Excel
	File management	No naming standard or classification. File management is unique to each researcher

Responsibilities, intellectual property rights

	Archiving and preservation (including storage and backup)
	Storage and backup during the	Personal computer
	project		Ollo Sib, Eric Vall
	preservation, localisation		No specific disposition
	Recommended lifetime		-
	Final volume of archived data	<100 MO
	Data to be destroyed		No
	Name of person in charge of archiving and preservation	Ollo Sib
	DMP for dataset(s)
	BF 2 Burkina
	Person in charge of the data collection	Ollo Sib
	Person in charge of the data analysis	Ollo Sib, Eric Vall
	Intellectual property rights	Governed by the CIRDES-CIRAD convention. A vérifier ?
	Aspects éthiques		Personal data about farmers
			No specific accord
	Data sharing, diffusion and reuse
	During the project	Acess limited to Burkina Faso team
	After the project	Open acess
	Type of license	https://creativecommons.org/choose/?lang=fr (aller choisir votre licence)
	Data repository	Not yet (feb. 2019)
	Data identifier	Not yet (feb. 2019)
	Dissemination date	Not yet (feb. 2019)
	Data reading	Excel
	Sensitive data	Personal data about farmers

Faso -Monthly monitoring of dairy cows' workshops Dataset(s) description

		Monthly monitoring of 15 farms: zootechnic data, milk production, cow
	Dataset description	weight measurements, what they eat, farrowing (18 months). In 2015-2017 Koumbia, Nasso, bobo Diolasso
	Nature of the dataset	Animal production data
	Method of production	Monitoring by survey and measurement, monitoring by farmer
	Tools for data processing	Excell
	Estimated volume of data	<100MO
	Existing publications related to this dataset	Not yet
	Re-use of existing data	No
	Intended future uses	Yes , scientific paper
	Users of data	Ollo sib Eric Vall

Faso -Agronomic monitoring of fodder banks Dataset(s) description

	Archiving and preservation (including storage and backup)
	Storage and backup during the	Personal computer
	Type of license Data repository Data identifier Dissemination date Data reading Sensitive data project preservation, localisation Recommended lifetime Final volume of archived data https://creativecommons.org/choose/?lang=fr (aller choisir votre licence) Not yet (feb. 2019) Not yet (feb. 2019) Not yet (feb. 2019) Excel Agronomic monitoring of fodder banks: data on growth measurements of Ollo Sib, Eric Vall No specific disposition -<100 MO Data to be destroyed No Name of person in charge of archiving and preservation Ollo sib BF 3 Burkina Dataset description perennial plants (height, diameter, number of leaves of shrubs), monitoring No on the production of leaves with cuts, soil analysis. Nature of the dataset Agronomic data BF 4 Burkina Faso -
	Method of production Archiving and preservation (including storage and backup) Field measurement, lab analysis Tools for data processing Excell
	Storage and backup during the Estimated volume of data	Personal computer <100MO
	project preservation, localisation Existing publications related to this dataset	Ollo Sib, Eric Vall No specific disposition Not yet
	Recommended lifetime Re-use of existing data		-No
	Final volume of archived data Intended future uses	<100 MO Scientific paper
	Data to be destroyed Name of person in charge of archiving and preservation Users of data	No Ollo sib Ollo sib Eric Vall
	Standards and metadata
	Metadata standards	No
	Data format	Excel
	File management	No naming standard or classification. File management is unique to each researcher
	Responsibilities, intellectual property rights
	Person in charge of the data collection	Ollo Sib
	Person in charge of the data analysis	Ollo Sib, Eric Vall
	Intellectual property rights	Governed by the CIRDES-CIRAD convention.
	Aspects éthiques		No sensitive data
	Data sharing, diffusion and reuse
	During the project	Access limited to Burkina Faso team
	After the project	Access limited to Burkina Faso team until publication of a paper (free acess after)
	Type of license	https://creativecommons.org/choose/?lang=fr (aller choisir votre licence)
	Data repository	Not yet (feb. 2019)
	Data identifier	Not yet (feb. 2019)
	Dissemination date	Not yet (feb. 2019)
	Data reading	Excel
	Sensitive data	No

Agronomic monitoring of fodder plots Dataset(s) description

	Data sharing, diffusion and reuse
	During the project	Access limited to Burkina Faso team
	After the project	Access limited to Burkina Faso teamuntil publication of a paper (free access after)
	Type of license	https://creativecommons.org/choose/?lang=fr (aller choisir votre licence)
	Data repository	Not yet (feb. 2019)
	Data identifier	Not yet (feb. 2019)
	Dissemination date	Not yet (feb. 2019)
	Data reading	Excel
	Sensitive data	No
	Archiving and preservation (including storage and backup)
	Storage and backup during the	Personal computer
	project		Ollo Sib, Eric Val, Patrice Kouakou
	Dataset description preservation, localisation Recommended lifetime Final volume of archived data Data to be destroyed Nature of the dataset Method of production Name of person in charge of archiving and preservation	No specific disposition Agronomic monitoring of fodder plots (itk, yields, environment) : corn -associated with cover crop. <100 MO 2016, Koumbia, Nasso, Bobo 13 fields No 2017, Koumbia, Nasso, Bana, Sourkoudinga, Bobo, 25 fields Agronomic data Field measurement, lab analysis Ollo Sib, Patrice Kouakou
	Tools for data processing		Excell
	Estimated volume of data		<100MO
	Existing publications related to this dataset	Not yet
	Re-use of existing data		No
	Intended future uses		Scientific paper
			Ollo sib
	Users of data DMP for dataset(s)		Eric Vall Patrice Kouakou
	Standards and metadata
	Metadata standards	No
	Data format	Excel
	File management	No naming standard or classification. File management is unique to each researcher
	Responsibilities, intellectual property rights
	Person in charge of the data collection	Ollo Si, Patrice Kouakou
	Person in charge of the data analysis	Ollo Sib, Eric Vall, Patrice Kouakou
	Intellectual property rights	Governed by the CIRDES-CIRAD convention.
	Aspects éthiques		No sensitive data

BF 5 Burkina Faso -Analysis of the network of local stakeholders involved in fodder crops Dataset(s) description

  During the projectAcess limited to Burkina Faso team After the project Acess limited to Burkina Faso team until publication of a paper (free acess after)

	Standards and metadata
	Metadata description	No
	Data format File management A1 Antilles -	Excel No naming standard or classification. File management is unique to each researcher
	Responsibilities, intellectual property rights
	Person in charge of the data collection	Ollo Si, Der Dabiré
	Person in charge of the data analysis	Ollo Sib, Eric Vall, Der Dabiré
	Intellectual property rights	Governed by the CIRDES-CIRAD convention.
	Aspects éthiques		No sensitive data
	Data sharing, diffusion and reuse
	Type of license
	Data repository	Not yet (feb. 2019)
	Data identifier	Not yet (feb. 2019)
	Dissemination date	Not yet (feb. 2019)
	Data reading	Excel
	Sensitive data	No
	Archiving and preservation (including storage and backup)
	Storage and backup during the	Personal computer
	project		Ollo Sib, Eric Vall
	preservation, localisation		No specific disposition
	Recommended lifetime		-
	Final volume of archived data	<100 MO
	Data to be destroyed		No
	Name of person in charge of archiving and preservation	Ollo Sib, Eric Vall
	Dataset description		Analysis of the networks of stakeholders involved in fodder production
	Nature of the dataset		Sociological data
	Method of production		Survey
	Tools for data processing		Word
	Estimated volume of data		<100MO
	Existing publications related to this dataset	No
	Re-use of existing data		No
	Intended future uses		No
	Users of data		Ollo sib Eric Vall

Effect of tomato-related cover on the production and presence of whiteflies Dataset(s) description

  Sauvadet Marie, Rhino Béatrice, Loisel Corentin, Floch Laura, Deberdt Peninna, Becquer Thierry, Tixier Philippe, Harmand Jean-Michel. 2018. Impacts of inter-row grass cover on soil biological fertility in tomato crop in Martinique. PoS2-52. In : Book of abstracts of the XV European Society for Agronomy Congress : "Innovative cropping and farming systems for high quality food production systems". Agroscope. Genève : Agroscope, Résumé, 149. European Society for Agronomy Congress (ESA 2018). 15, Genève, Suisse, 27 Août 2018/31 Août 2018. Re-use of existing data

		Experimental results on Tomato associated with service plants: abundance
	Dataset description	of pests and auxiliaries, yield…
		Martinique
	Nature of the dataset	Agronomic data
	Method of production	Field measurements and observations
	Tools for data processing	Excel
	Estimated volume of data	<100 Mo
	Existing publications related to
	this dataset
		No
	Intended future uses	Possibility of reuse by researchers for closed research questions to compare data and results
	Users of data	Béatrice Rhino

for the TRAnsition to bio-DIVersified agroecosystems, from process analysis to multi-scale co-conception with actors.

Name of the funder(s)

Agropolis Fondation

Call Identifier

Flagship project

Topic of the call Agro-ecosystem management

Grant agreement number 1504-

Data identifier

DOI CIrad

Dissemination date

Not yet Data reading

Excel

Sensitive data

No ?

Archiving and preservation (including storage and backup)

CR1 Costa Rica Evaluation of cover plant -Costa Rica

Dataset(s) description

Dataset description

Evaluation of cover crops 20 farms: topoclimatic conditions, coffee plant physiology and yield (age, size, practices), characteristics of cover plants: litter, % soil cover. Species "native" or selected by producers. Botanical description of native species. Inputs (pesticides), practices (method of weed control, number of passages), working time, and cost of production. Area of Turialba. Over 6 months, 1 pass per farm. Measurement of light transmission to the ground (densiometer).

Nature of the dataset

Agronomic data

Method of production

Farm survey , field observation Tools for data processing

Excell ? Estimated volume of data <100MO Existing publications related to this dataset ?

Re-use of existing data ? Intended future uses ? Users of data Scientific community and public ?

Standards and metadata

Metadata description

No Data format

Excell File management

No naming standard or classification. File management is unique to each researcher

Responsibilities, intellectual property rights

Person in charge of the data collection Karel van den meersche

Person in charge of the data analysis Karel van den meersche

Intellectual property rights

Governed by the CATIE-CIRAD convention

Aspects éthiques

Est-ce qu'il y a une référence à des accords particuliers du type accès aux connaissance autochtone, savoir traditionnel, biodiversité ?

Data sharing, diffusion and reuse

No publication at the moment

Re-use of existing data

No existing data reused

Intended future uses Possibility of reuse by researchers for closed research questions to compare data and results

Users of data

Scientific community

Standards and metadata

Metadata standards

Crop ontology, Darwin core, eml Data format Excel, txt, jpg

File management

File management is unique to each researcher

Responsibilities, intellectual property rights

Person in charge of the data collection Martin Notaro

Person in charge of the data analysis Martin Notaro

Intellectual property rights CIRAD Aspects éthiques

Data sharing, diffusion and reuse

N4 Nicaragua Follow-up co-design UCA

Dataset(s) description

Dataset description

Data on about 30 producers over 1 year, type characterization of shade: impact on aladies and pests, fertility and production. Observations (counts, ..) made by producers. Measures simpler than Camille proctocole. Soil analysis.

Nature of the dataset

Surveys and observations

Method of production

Surveys protocols Tools for data processing Excel Estimated volume of data <100Mo Existing publications related to this dataset

No publication at the moment

Re-use of existing data

No existing data reused

Intended future uses

Possibility of reuse by researchers for closed research questions to compare data and results

Users of data

Scientific community

Standards and metadata

Metadata standards DDI Data format

Excel, txt

File management

File management is unique to each researcher

Responsibilities, intellectual property rights

Person in charge of the data collection Clémentine Alline

Person in charge of the data analysis Clémentine Alline

Intellectual property rights Accord Cirad -partenaire du Nicaragua ?

Aspects éthiques

Protocol to manage personal data (anonymization?)

Data sharing, diffusion and reuse

C1 Cameroon -Cocoa agroforestery systems chronosequences

Dataset(s) description

Dataset description

Biophysical variables, harvested between 2015 and 2018, on 42 samples, 1 sample = a plot of 800 m2 cocoa agroforestry system, single catch data (1 data per plot per variable): biomasses of several compartments, diversity and species specific richness, Cocoa yields, measurements of functional features (tree leaves), cocoa architecture (typology), soil physico-chemical analysis (0.15 and 15-30 cm).

Responsibilities, intellectual property rights

Person in charge of the data collection Stéphane Saj

Person in charge of the data analysis Stéphane Saj

Intellectual property rights CIRAD

Aspects éthiques

The anonymity of the owners of the agroforestery systems studied must remain guaranteed.

Data sharing, diffusion and reuse

External Data set description list

Please list and describe any existing datasets which will be used for the research to be carried out (third party data sources). Please also identify any specific issues relating to access to these data and how you will overcome any difficulties.

Dataset name

Persistent


R Gauchi

V Egloff

M Kooli

J.-P Noel

B Giraud

P Vivet

S Mitra

H.-P Charles

Reconfigurable Tiles of Computing-In-Memory SRAM Architecture for Scalable Vectorization

Keywords: Computing-In-Memory, SRAM, Instruction Set Simulator, System-C/TLM, SIMD, PolyBench

For big data applications, bringing computation to the memory is expected to reduce drastically data transfers, which can be done using recent concepts of Computing-In-Memory (CIM). To address kernels with larger memory data sets, we propose a reconfigurable tilebased architecture composed of Computational-SRAM (C-SRAM) tiles, each enabling arithmetic and logic operations within the memory. The proposed horizontal scalability and vertical data communication are combined to select the optimal vector width for maximum performance. These schemes allow to use vector-based kernels available on existing SIMD engines onto the targeted CIM architecture. For architecture exploration, we propose an instruction-accurate simulation platform using SystemC/TLM to quantify performance and energy of various kernels. For detailed performance evaluation, the platform is calibrated with data extracted from the Place&Route C-SRAM circuit, designed in 22nm FDSOI technology. Compared to 512-bit SIMD architecture, the proposed CIM architecture achieves an EDP reduction up to 60× and 34× for memory bound kernels and for compute bound kernels, respectively.

INTRODUCTION

As artificial intelligence progresses, today's applications are becoming data and computationally intensive. The cost of transferring data from the memory to the processing element is very high compared to the cost of the computation itself [START_REF] Horowitz | 1.1 Computing's energy problem (and what we can do about it)[END_REF]. New emerging architectures must be considered to reverse this paradigm by bringing the computation closer to the memory. In-Memory Computing (IMC) has been introduced in [START_REF] Charles | X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories[END_REF][START_REF] Akyel | DRC2: Dynamically Reconfigurable Computing Circuit based on memory architecture[END_REF][START_REF] Wang | A Compute SRAM with Bit-Serial Integer/Floating-Point Operations for Programmable In-Memory Vector Acceleration[END_REF] and aims at bringing the computation as close as possible to the memory, by directly modifying the SRAM bit-cell design. These IMC designs can perform logic operations (mainly bit-wise) within the memory by selecting multiple word-line of the memory bit-cell array. In order to provide more complex logic and arithmetic operations, the Near-Memory Computing (NMC) provides the necessary additional operators inside the SRAM memory tile, as presented in [START_REF] Eckert | Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks[END_REF][START_REF] Wang | A Compute SRAM with Bit-Serial Integer/Floating-Point Operations for Programmable In-Memory Vector Acceleration[END_REF]. From a system integration and software-level, IMC and NMC can be named as Computing-In-Memory (CIM), since all computations are performed within the memory interface. Thus, we refer IMC and NMC as CIM in the rest of this paper and we consider the Computational-SRAM (C-SRAM) concept as introduced in [START_REF] Akyel | DRC2: Dynamically Reconfigurable Computing Circuit based on memory architecture[END_REF]. It represents a SRAM tile integrating additional logic allowing arithmetic and logic operations. In all these schemes, data locality is preserved and acceleration is performed through parallel computing, using vector operations, where vector width corresponding to SRAM width. Recent work on SRAM-based CIM has focused on computing within a single or a few memory tiles, which drastically limits the memory capacity and potential applications. In order to address kernels with larger dataset and real applications, it is required to scale up the architecture with more memory, while proposing the adequate programming model allowing vector acceleration. With more available memory, a question raises: how to organize these tiles of memory to perform large vector operations : more vectors or larger vectors ?

In order to increase the available memory space and associated computing capability, we propose to assemble a set of C-SRAM tiles in a configurable fashion. This allows to extend computing vector in two manners: either a horizontal memory extension allowing larger vectors, or a vertical memory extension allowing more vectors. This scalable vectorization scheme allows to directly re-use existing SIMD (Single Instruction Multiple Data) application kernels onto the targeted C-SRAM architecture. In order to evaluate this architecture, we propose an instruction accurate simulation platform that considers all transaction events between hardware components and its integration into a standard software tool chain. For accurate architecture exploration, all low level performance numbers (cycles and energies) are extracted from Place&Route implementation in 22nm FDSOI technology, as presented in [START_REF] Noel | Computational SRAM Design Automation using Pushed-Rule Bitcells for Energy-Efficient Vector Processing[END_REF]. Using the simulation platform, we evaluate the performance speed up, the energy reduction and the Energy Delay Product (EDP) reduction of the reconfigurable vector-based C-SRAM tiles architecture compared to tightly-coupled 128-bit, 256-bit and 512-bit SIMD architectures, using the same vectorized kernels. Simulation results show that our proposed architecture achieves an EDP reduction up to 60× and 34× for memory bound kernels and for compute bound kernels respectively, compared to 512-bit SIMD architecture. The main contributions of this paper are:

• Proposing a reconfigurable multi-tiles of C-SRAM architecture for scalable vectorization, • Proposing an instruction accurate and power annotated simulation platform for multi-tile C-SRAM evaluation, • Comparing performance speed up, memory accesses, energy reduction and energy-delay product of our proposed architecture with regards to modern SIMD architectures.

The remainder of this paper is organized as follows: Section 2 discusses the state-of-the-art. Section 3 introduces the proposed architecture, Section 4 details the simulation platform, Section 5 explains architecture exploration results, and finally, Section 6 concludes the paper.

RELATED WORK 2.1 In and Near Memory Computing

IMC and NMC concepts have been introduced recently and propose different kind of data-centric architectures. From a circuit design point of view, many recent works [START_REF] Charles | X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories[END_REF][START_REF] Akyel | DRC2: Dynamically Reconfigurable Computing Circuit based on memory architecture[END_REF][START_REF] Dong | A 4 + 2T SRAM for Searching and In-Memory Computing With 0.3-V VDDmin[END_REF][START_REF] Wang | A Compute SRAM with Bit-Serial Integer/Floating-Point Operations for Programmable In-Memory Vector Acceleration[END_REF] have shown that IMC provides logic operators using pre-computation capabilities within the memory array. Recent works [START_REF] Hamdioui | Memristor based computation-in-memory architecture for data-intensive applications[END_REF] also explored IMC using emerging technologies such as Non Volatile Memory (NVM) to perform Multiply And Accumulate (MAC) operations by exploiting analog current laws. Such circuits targets neural network applications, while our architecture uses existing current CMOS technology SRAM memories to target general purpose applications, specifically with vector level parallelism. Compared to IMC, NMC consists of an optimized design placed close to the memory which performs operation between several memory tiles. But, this "near memory logic" is usually not tightly integrated within the memory, nor respecting a standard memory interface for smooth software integration, as presented in [START_REF] Singh | A Review of Near-Memory Computing Architectures: Opportunities and Challenges[END_REF]. A specific IMC type, using bit serial scheme has shown up 1.9× speed up gain and 2.4× energy reduction, but requires a high data parallelism (all words active) to achieve these gains, as presented in [START_REF] Eckert | Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks[END_REF][START_REF] Wang | A Compute SRAM with Bit-Serial Integer/Floating-Point Operations for Programmable In-Memory Vector Acceleration[END_REF]. For further system integration, IMC feature has been integrated within cache architecture as proposed in [START_REF] Andrew | BLADE: A BitLine Accelerator for Devices on the Edge[END_REF], nevertheless, the programming model of IMC through the cache is not explicit and large vector operations are not supported. For ease of software integration, specific Instruction Set Architecture (ISA) for IMC has been proposed and evaluated at functional-level on a single IMC memory instance in [START_REF] Kooli | Smart instruction codes for in-memory computing architectures compatible with standard SRAM interfaces[END_REF]. For larger dataset, integration of multiple tiles of IMC or NMC has not been explored in a systematic way in terms of memory organization and performance trade-offs. In this paper, we propose a reconfigurable organization of CIM memory tiles, for vector based parallelism acceleration using a standard programming model.

Reconfigurable Architecture

For reconfigurable computing, FPGAs are composed of tiles of logic, using Look-Up-Table (LUT) and tiles of SRAM, called Block RAM (BRAM). FPGAs are designed for emulating any logic and memory functions while the proposed reconfigurable C-SRAM tiles are made of rather similar elements, but organized to act as an energy efficient vector accelerator. In past years, many vector processors and associated configurable architectures have been proposed [START_REF] Stephens | The ARM Scalable Vector Extension[END_REF], but they still rely on a register semantic and their vector width is limited to their local SRAM data bus. The proposed reconfigurable C-SRAM tiles architecture avoids register transfers by maintaining data within the local SRAMs and the vector width can be configured to the maximum number of available tiles.

RECONFIGURABLE CIM ARCHITECTURE 3.1 Architecture Overview

As shown in Figure 1, the C-SRAM cluster consists of multiple tiles of C-SRAM disposed in a physical array. Each C-SRAM tile is composed of : the SRAM itself, a Local ALU (Arithmetic Logic Unit) for near-memory operation, a Local FSM (Finite State Machine) for pipeline control and finally a Vertical Transfer unit. For application mapping facility, the physical C-SRAM array can be logically reconfigured in two manners : either for horizontal vector extension to create larger vectors using the Tile Address Mapper unit ; or for vertical vector extension to create more vectors using the vertical connections and the Vertical Transfer unit. Each C-SRAM tile stores and computes data on a 128-bit vector width in one instruction. Using the horizontal scalability, up to 2048-bit vectors can be handled in a single cycle. We detail these mechanisms in section 3.2.

For overall control at cluster level, the total vector width is configured by the Tile Address Mapper unit, while the Global Pipeline Dispatcher unit resolves data hazards and conflicts between interleaved SRAM accesses and CIM instructions. Every CIM instruction passes through a 5-stage pipeline made up of (1) Decode, (2) Read left operand, (3) Read right operand, (4) Execute the operation and (5) Write back into the memory. This C-SRAM cluster array is seen as an co-processor unit, and is integrated onto a standard system bus. It is controlled by a CPU (Central Processing Unit) engine with its tightly coupled data and instruction memories.

Inter-tiles Reconfiguration and Vertical Communication

For data-centric kernel, such as matrix multiplication shown in Listing 1, reduction operations (perform the same operation between the elements composing the vector) can be costly when computing on large vectors. The proposed architecture can dynamically resize the vector width during execution for each instruction. Actually, as shown in Figure 2, the kernel defines logical vector width used as in the layout ➌, then the next instruction uses sub-vectors as in the layout ➋. After performing an addition between these subvectors, the last instruction splits the vector as in ➊, to complete the operation reduction in a 512-bit vector. To maintain instruction throughput, the Tile Address Mapper ensures the vertical data movement between tiles and the Global Pipeline Dispatcher orders instruction to avoid pipeline data hazards. Tile Address Mapper -Assembling these C-SRAM tiles horizontally permits to realize vectorized computation by distributing the same CIM instruction to all the tiles. Moreover, the Tile Address Mapper allows 32-bit sequential access to all C-SRAM in a horizontal way. In Figure 2, the vector size is scalable from 512 bits up to 2048 bits with a 128-bit step, thus offering an address range that extends from 1024 vectors of 512 bits to 256 vectors of 2048 bits. Thanks to this vector approach, computations are performed in an aligned and vertical way. By using a memory partitioning into smaller tiles, the Tile Address Mapper allows to scale the architecture with some limits: energy cost of individual accesses is inversely proportional to the read access time. In terms of physical design, the Tile Address Mapper provides an energy/performance trade-off as long as the number of tiles is limited [START_REF] Gauchi | Memory Sizing of a Scalable SRAM In-Memory Computing Tile Based Architecture[END_REF].

Vertical Transfer Multiplexer -For data transfer between tiles, two additional vertical interconnections of 128 bits width each redirect a large vector of data respectively in the upper or lower direction. For example, in tiling configuration ➋, a vector that moves from tiles A to tiles B implies that the 1024-bit data will be transferred through rows of multiplexer via the down path of eight C-SRAM tiles. The vertical transfer is done in the second pipeline stage, within the read cycle, before the computation cycle.

Data placement and off-chip memory -To read or write a 32bit data from the CPU, the Tile Address Mapper selects the aligned C-SRAM according to various layout set-up, as presented in Figure 2. At software-level, the Tile Address Mapper can stride addresses in memory to optimize internal data transfer which impacts the vectorization performance. In the proposed architecture, all memories (SRAM and C-SRAM) are on-chip memories in order to evaluate kernel acceleration. Nevertheless, data movement from off-chip memories should also be considered to evaluate larger datasets.

ISA and Programming Model

To use an abstract model from the user's point of view, a dedicated ISA for the IMC architecture is detailed in [START_REF] Kooli | Smart instruction codes for in-memory computing architectures compatible with standard SRAM interfaces[END_REF]. In this paper, we demonstrate the extensibility of this ISA with a multi-tile implementation, without changing the design of the CPU and couple our multi-tile architecture on a standard 32-bit system bus. From the execution point a view, sending a compute instruction to the C-SRAM is equivalent to writing a specific data to a specific address in a memory (store instruction). From the software-level point of view, we implement our C-SRAM ISA as a library written in C/C++, as written in Listing 2, that can be included in any C/C++ compiler tool chain (clang, gcc, riscv-gcc). In addition, we define a new vector format type (CSRAM_Vect_t) which ensures the data alignment inside the targeted memory area, as proposed in Listing 1. This method is used for both SIMD and CIM software evaluation. Thus, macro definitions as in Listing 1 and inlined functions allow to be SIMD-compatible whatever the vector size is. Since the same level of vector parallelism is targeted, the proposed programming model permits to reuse directly low level vectorized kernels already written for SIMD architecture for our proposed CIM architecture. This avoids the tedious work of application mapping and parallelism extraction.

SIMULATION PLATFORM

Previous works on IMC have used Low Level Virtual Machine (LLVM) as a high level compiler tool chain for high level performance profiling of the IMC [START_REF] Kooli | Software Platform Dedicated for In-Memory Computing Circuit Evaluation[END_REF]. Nevertheless, it lacks detailed modeling of instruction control flow and memory sizing with regards to application needs. In order to provide a fast and flexible modeling platform of the architecture, we propose in this paper a simulation platform using SystemC Transaction-Level Modeling (TLM) abstraction [START_REF]IEEE Standard for Standard SystemC Language Reference Manual. IEEE Std 1666-2011[END_REF]. In order to perform an accurate exploration at system-level, we extract all performance and energy values from Place&Route design experiments using Global Foundries (GF) 22nm FDSOI technology node.

Simulation Platform Overview

The proposed simulation platform is composed of both hardware and software related layers, as shown in Figure 3. On the top, the software layer relies on the C/C++ C-SRAM library, a Linker Descriptor (LD) script and the user program in a GCC tool chain to create an Executable and Linkable Format (ELF) file. On the bottom, the hardware layer simulates the architecture behaviour and memory transactions between hardware components, as presented in Section 3. Software Layer -To run our target applications on our platform, we use the same riscv-gcc compiler tool chain as the actual hardware. The C-SRAM ISA library consists of macro definitions and inlined functions to emulate a vector format such as SIMD, but expandable up to 8192 bits wide. The LD script file define the memory map by specifying the program (text), the data (SRAM) and the CIM (C-SRAM) sections.

Hardware Layer -For RISC-V modeling, we use an open source instruction-accurate Instruction Set Simulator (ISS) developed by Western Digital [START_REF]Western Digital's Open Source RISC-V SweRV Instruction Set Simulator[END_REF]. This ISS is wrapped in SystemC/TLM platform and execute the binary ELF file given by the compiler. By using SystemC/TLM, we model all other hardware components with cycle accuracy, including the C-SRAM, the Global Pipeline Dispatcher, the Tile Adress Mapper, and all other sub-system accesses and associated latencies. For power modeling, we inherit the TLM Power library [START_REF] Lebreton | Power Modeling in SystemC at Transaction Level, Application to a DVFS Architecture[END_REF], allowing to annotate power values of all elements, including idle state and leakage values.

Simulation Platform Calibration

Regarding the physical scalability of the multi-tile memory architecture, we have evaluated the wiring cost and the correct trade-off between C-SRAM tile size and tile performance. As presented in [START_REF] Gauchi | Memory Sizing of a Scalable SRAM In-Memory Computing Tile Based Architecture[END_REF], a wiring cost and energy model based on Place&Route shows the scalability of multiple SRAM tiles: for a 256 kB of total memory size, composing an 4×16 array of 4 kB tile, the wiring cost between tiles is about 50% in read access time, while partitioning in array allows to save massive dynamic power. The C-SRAM architecture [START_REF] Christmann | A 50.5 ns Wake-Up-Latency 11.2 pJ/Inst Asynchronous Wake-Up Controller in FDSOI 28 nm[END_REF] in GF 22nm, C-SRAM numbers are from Place&Route results under GF 22nm in [START_REF] Noel | Computational SRAM Design Automation using Pushed-Rule Bitcells for Energy-Efficient Vector Processing[END_REF] and SIMD instruction numbers are adapted according to the bus width as explained in Section 5.2.

integrates both IMC operations (bitwise logic) and NMC operations (ADD, MULT ). It has been designed in a GF 22nm FDSOI technology and implemented with different floor-plans configurations up to final Place&Route [START_REF] Noel | Computational SRAM Design Automation using Pushed-Rule Bitcells for Energy-Efficient Vector Processing[END_REF]. We have selected a 4 kB SRAM configuration: the C-SRAM single tile performance numbers (timing and energy access) are extracted for the multi-tile architecture exploration. In this paper, we ensure that all evaluated architectures operate at the same frequency and each timings (for memories and core) are constrained using the same parameters, in order to extract energy values summarized in the Table 1.

As a summary of all related design performances, each row of Table 1 represents a hardware component converted into TLM module that have different energy states (Compute, Fetch, Read, Write). For instance, all instructions perform by the RISC-V core (Compute) are fetched (Fetch) from the SRAM (instruction) memory and all CIM instructions are executed in each C-SRAM (Compute). These energy values are given according to the instruction types.

ARCHITECTURE BENCHMARKING

Using the proposed simulation platform, we study five kernels representing different application profiles in terms of memory patterns and computing requirement. All our application evaluations use the same vectorized code using gcc (7.4.0) and riscv-gcc (7.2.0) compilers at optimization level 3, forcing SIMD vectorization by using intrinsics on Intel's Xeon processor.

Kernels with Scalable Vectorization

Hamming Weight (hw) [START_REF] Faro | The Exact Online String Matching Problem: a Review of the Most Recent Results[END_REF] is a kernel used in information theory applications to count the number of ones after performing a XOR operation between two vector of bits (using AND, SRL, ADD, SUB operators). Shift-OR (so) [START_REF] Faro | The Exact Online String Matching Problem: a Review of the Most Recent Results[END_REF], is a kernel used in bio-informatic applications to match a pattern in a DNA sequence. It prepares a set of bit-masks saving each position of the pattern element and shifts those bit-masks through the DNA sequence with bitwise operators (OR, AND, SRL, SLL) to match the position. PolyBench [START_REF] Pouchet | PolyBench/C, the Polyhedral Benchmark suite[END_REF] is a collection of benchmarks used for evaluating polyhedral problems and improve auto-parallelization of compilers. By using 8bit fixed-point addition and multiplication instructions, we assess three Basic Linear Algebra Sub-programs as: atax, a matrix transpose and vector multiplication kernel as 𝐴 𝑇 .𝐴 𝑥 , gemm, a matrixmultiply as 𝐶 = 𝛼 .𝐴.𝐵 + 𝛽.𝐶 kernel and 3mm, three matrix-multiply (k) as 𝐸 = 𝐴.𝐵; 𝐹 = 𝐶.𝐷; 𝐺 = 𝐸.𝐹 . These five kernels represent different application profiles in terms of memory patterns and computing requirement.

SIMD Comparison Methodology

To evaluate SIMD programs on Intel's SIMD processors, we use Pin-3.11 [START_REF] Lueck | PinADX: An interface for customizable debugging with dynamic instrumentation[END_REF], a dynamic binary instrumentation program developed by Intel. Thanks to the set of analysis tools provided, we extract and log CPU/SIMD instructions and memory accesses sorted by vector width for SSE2 (SIMD 128 bits), AVX-2 (SIMD 256 bits) and AVX-512 (SIMD 512 bits). Using this instruction level profiling, we obtain an instruction level analysis and, based on values established in Table 1, we evaluate each SIMD power state proportional to the bus width access. For example, an instruction of 512-bit SIMD access to 16 data of 32 bits is equivalent in energy to 16 times the memory access of the RISC-V core. All SIMD instructions including control flow and standard memory accesses are also counted and their cost evaluated.

Architecture Exploration Results

In Figure 4, we compare four architectures against the baseline RISC-V scalar architecture for different data set sizes and report execution speed up, memory requests and energy reduction. For both SIMD and CIM architectures, all data are pre-loaded in their 256 kB associated SRAMs, and the CIM cluster set-up is a 4×16 array of 4 kB tile enabling an horizontal scalability of 512-bit up to 8192bit vector width. For each data set size, the maximum vectorization width is configured for each architecture and the labels in the speed up results represent the CIM vector width used. For small data set (8 kB and 16 kB), vectorization is limited to 512 bits, where CIM and SIMD architectures are compared for the same 512-bit width. For larger data sets, the largest CIM vector width is selected (up to 8192 bits) while compared to the maximum SIMD available width.

Regarding the Table 2, the so and hw kernels refer as compute bound kernels and atax, gemm and 3mm refer as memory bound kernels.

Speed Up Exploration (a,b,c,d,e) -At equivalent vector width (8 kB and 16 kB), the execution speed up shows that the CIM architecture has gains close to the 512-bit SIMD architecture. This minor difference is due to the additional control flow of the proposed ISA, necessary to execute the instructions in memory (constants, loop preparation). However, the CIM architecture continues to increase its speed up using larger vectors, wider than 512 bits for larger data sets contrary to SIMD architectures, limited to 512 bits. Data dependencies in memory bound kernel are mostly resolved by using the dynamic tiling configurations from 8192-bit down to 512-bit for reduction operations as explained in 3.2.

External Memory Bandwidth Limitation (a,b,c,e) -For modeling external memory access, we propose a simple bandwidth limitation using an 10 GB/s memory bandwidth (LPDDR4). By considering the data movement from an external memory, the top-line represents the minimum time to transfer the data set in the cluster compared to the execution time. For all kernels besides hw and atax, this limitation is not reached, which means that enough memory bandwidth is available to feed the kernel. Even using CIM architecture, off-chip memory accesses is still a concern. Memory Requests Exploration (f,g,h,i,j) -The CIM architecture strongly reduces the number of standard memory requests (read/write) with the CPU. However, for memory bound kernel, additional memory accesses are still required to complete the reduction operation. For a data set size of 256 kB, the vectorization of the 3mm kernel allows a maximum vector of 2048 bits with the CIM architecture, thus having more reduction operation and smaller speed up gain than the other kernel for this data set size.

Energy Reduction Exploration (k,l,m,n,o) -Contrary to the SIMD architecture, the CIM architecture continues to increase its speed up while still maintaining a high energy reduction, thanks to the reduction of data movement between processor and memory replaced by the vertical data transfers between C-SRAM tiles. By using an 8192-bit vector width on atax kernel, the CIM architecture achieves energy reduction of 75× and 4.3× compared to the scalar baseline and the 512-bit SIMD architectures respectively.

Energy Delay Product Reduction Exploration -The Figure 5 presents the relative EDP reduction of the CIM architecture compared to SIMD 512 bits architecture using the same exploration methodology as in Figure 4. For memory bound kernels, an EDP reduction up to 60× is achieved, while for compute bound kernels, where more control flow is still required, an EDP reduction of 34× is achieved. Finally, the EDP gain of hw kernel for the CIM architecture is limited due to the external memory bandwidth limitation as discussed before.

CONCLUSION

In this paper, we proposed a reconfigurable and scalable vector-tilebased architecture and a simulation platform to evaluate the benefits of CIM. Our solution allows to implement computing applications requiring vector acceleration, without changing the overall architecture and reusing vectorized kernels already written for SIMD architectures. For each kernel, performance gains are achieved thanks to large vectorization in line with the application kernel. The proposed horizontal reconfigurability and vertical communication scheme allow to select the optimal vectorization width for maximum performance, while the SIMD is limited with the maximum width of 512 bits. Using equivalent vector-based instructions, compared to 512-bit SIMD architecture, the CIM architecture achieves an EDP reduction up to 60× and 34× for memory bound kernels and for compute bound kernels, respectively. This architecture is a possible option towards refining the N3XT architecture [START_REF] Sabry Aly | The N3XT Approach to Energy-Efficient Abundant-Data Computing[END_REF], integrating fine grain distributed computing within the 3D memory-computing system. For future works, we will implement the physical design of our reconfigurable vector-tile-based CIM architecture, compare it to other vector architectures, and explore kernels with 3D data movement using larger data sets.

Figure 1 :

 1 Figure 1: Reconfigurable Tiles of C-SRAM Architecture, integrated with a CPU on a standard system bus.

Figure 2 :

 2 Figure 2: Physical and logical view for 3 vector configurations: ➊ 512-bit, ➋ 1024-bit and ➌ 2048-bit wide.

Listing 1 :

 1 Example of Matrix Multiply kernel in language C.

Figure 3 :

 3 Figure 3: Hardware/Software Simulation Platform.

Figure 4 :

 4 Figure4: Simulation results of execution speed up, memory requests and energy reduction of five kernels, compared to the scalar architecture (RISC-V with SRAM). Each label (in Speed Up) represents the CIM vector width (in bit) for each data set size.

Figure 5 :

 5 Figure 5: Simulation results of EDP reduction compared to the 512-bit SIMD. Each label is the CIM vector width used (in bit).

Table 1 :

 1 Energy details in 22nm FDSOI used for simulations. SRAM memory access in one instruction. Notes: RISC-V numbers are adapted from

		Average of extracted energy values (pJ)
	Module name	Core		Memory
		Compute(s) Fetch Read	Write
	Inter-connect (on-chip)	-	1.63	1.63	1.63
	RISC-V core	[2.24, 4.83]	1.92	-	-
	SRAM (4kB, 32-bit access)	-	-	4.20	4.20
	128-bit C-SRAM (4kB, 32-bit access) [3.41, 7.21]	-	4.32	6.48
	128-bit SIMD (128-bit access)	[8.96, 19.32]	1.92	16.80 *	16.80 *
	256-bit SIMD (256-bit access)	[17.92, 38.64] 1.92	33.60 *	33.60 *
	512-bit SIMD (512-bit access)	[35.84, 77.28] 1.92	67.20 *	67.20 *

*

Table 2 :

 2 Memory accesses / instruction analysis of the scalar baseline to identify memory and compute bound kernels.

	Kernel	hw	so	atax	gemm	3mm
	Memory Accesses (%)	9.1	2.3	27.6	37.1	28.4
	Compute Instructions (%) 90.9	97.7	72.4	62.9	71.6


C Hubert

L Billot

P.-M Adam

R Bachelot

P Royer

J Grand

D Gindre

K D Dorkenoo

A Fort

Role of surface plasmon in second harmonic generation from gold nanorods

The role of surface plasmon in second harmonic generation from arrays of gold nanorod particles excited by femtosecond laser pulses is investigated as a function of incident light polarization and irradiation wavelength. In addition to photoluminescence, a peak of second harmonic is observed and is found to depend on the polarization and wavelength of the fundamental frequency laser beam. In particular, the authors found similarities between extinction spectra of the nanoparticles and spectra of emmitted second harmonic. This behavior can be explained by resonant excitation of localized surface plasmon resonances. © 2007 American Institute of Physics. ͓DOI: 10.1063/1.2734503͔

The linear optical properties of metallic nanoparticles ͑MNs͒ are dominated by collective oscillations of the conduction electrons. In particular, noble MNs present localized surface plasmon resonances ͑LSPRs͒ that lead to a strong absorption/scattering and local field enhancement near such structures. 1 The spectral position of these resonances depends on the particles shape and size as well as on the nature of the particle and the refractive index of the surrounding medium. 2 Together with their linear properties, nonlinear optical properties of metallic nanoparticles were studied and appear promising for photonic applications. [START_REF] Tominaga | [END_REF] Because of symmetry considerations, second harmonic generation ͑SHG͒ is forbidden for centrosymmetric systems and thus strongly depends on defects and small deviations from the symmetric shape as well as from broken symmetry at interfaces. Nonlinear studies have been reported in the case of metal colloids and tips through measurements of hyper-Rayleigh scattering and SHG. [4][5][6] SHG from nanostructures with a low degree of symmetry and from noncentrosymmetrical structures composed of symmetrically shaped particles was also investigated. [7][8][9] Polarization studies have shown that SHG can be enhanced via resonant excitation of LSPR ͑Refs. 9-11͒ of nanoparticles. More recently, SHG measurements of planar symmetrical structures were achieved using non-normal incidence illumination 12 or by looking at angles other than the illumination direction. 10 So far, no spectral study of localized surface plasmons on nanoparticles has been reported using SHG as a probe. In this letter, we present a spectral study of SHG from periodic arrays of gold nanorods. We demonstrate that SHG strongly depends on the incident light polarization direction and that its excitation spectroscopy unambiguously evidences the role of LSPR in the second harmonic signal enhancement.

Figure 1͑a͒ shows the experimental setup used to generate and detect the second harmonic signals. A femtosecond Ti:sapphire laser beam ͑100 fs, 80 MHz repetition rate͒ is focused onto the nanostructures using a ϫ20 microscope objective into a wide laser spot of 1.3 m in diameter. The pump wavelength can be tuned from 740 to 860 nm. The a͒ Author to whom correspondence should be addressed; Present address: Laboratoire Hubert Curien, UMR CNRS 5516, Université Jean Monnet, 18 rue Pr Benoît Lauras, 4200 Saint-Etienne, Cedex 2, France; electronic mail: christophe.hubert@univ-st-etienne.fr incident polarization direction and power are controlled with a half-wave plate and a Glan-Taylor polarizer. The sample is moved in the focal plane of the first objective microscope using three-dimensional microdisplacement. The SHG signal is collected in transmission through the sample with a second ϫ20 microscope, and a BG 39 Schott filter is used to eliminate the fundamental beam. The SHG spectra are measured by a spectrometer and a charge coupled device. The insert in Fig. 1͑b͒ shows a scanning electron microscope image of the nanostructures fabricated by electron beam lithography through the lift-off method. 6 Spacing between the ellipses is kept constant and equal to 200 nm both in the x and y directions. We chose this edge to edge distance so that no strong near-field coupling nor any grating effect ͑i.e., far-field cou-pling͒ are observed in the extinction spectra. Because of this, and also because of the homogeneity in trust the extinction spectra to reflect the optical properties of one particle and believe that we are probing a single particle response. The long axis length is varied from 150 to 190 nm, whereas the short axis and the height of the nanostructures are equal to 50 and 60 nm, respectively. Figure 1͑b͒ shows the excitation power dependence of the SHG signals measured on gold nanorods by fitting a Lorentzian to a series of spectra. The linear fit of the variation of the emitted signal versus pump power in a logarithmic scale shows that the second harmonic signal has a nearly quadratic dependence on the excitation intensity ͑slope: 1.9͒, a characteristic of second order nonlinear processes. The extinction spectra were recorded using a spectrometer coupled to a microscope by means of an optical fiber.

Figure 2 shows the extinction spectra from arrays of nanorod particles. The maxima that peak at 776 and 795 nm correspond to nanoparticles with long axes of 150 and 170 nm, respectively, and are associated with the long axis resonance mode of the nanostructures. As can be observed, the peak of the LSPR associated with the nanorod long axis is tuned to the laser wavelength at 800 nm. The resonance features in the extinction spectrum along the short axis at wavelengths greater than 800 nm cannot be attributed to a plasmon resonance and even though their origin has not been clearly established yet, they do not reflect any particular physical property of the nanoparticle themselves.

Figure 3͑a͒ shows the typical spectra obtained when illuminating gold nanorods of 150 nm long axis with the femtosecond 800 nm pump beam. The polarization angle of the incident laser beam is defined with respect to the nanorod long axis, as indicated in the insert of Fig. 3͑a͒. The maximum intensity of the second harmonic generation signal is obtained when the incident light polarization direction is parallel to the nanorod long axis ͑ =0°͒, i.e., when the LSPR associated with the long axis of the nanoparticles is excited. On the contrary, when the polarization angle is equal to 90°, no second harmonic signal is detected. The spectrum corresponding to = 0°contains, in addition to the SHG signal, the beginning of a broad peak centered at a higher wavelength falling outside of our detection window. This corresponds to the photoluminescence of the nanoparticles under two-photon excitation and has been already observed. 13,14 SHG intensity I ͑2͒ is proportional to the squared second order nonlinear polarization P ͑2͒ and, in the case of metallic nanoparticles, I ͑2͒ is proportional to 15

I ͑2͒ ϰ f ͑͒ 4 f ͑2͒ 2 I ͑͒ 2 , ͑1͒
where f ͑͒ is the local field enhancement factor at the fundamental frequency, f ͑2͒ the local field enhancement factor at the second harmonic frequency, and I ͑͒ the pump beam intensity. In the case of nanorods, f ͑͒ can result from both LSPR and off-resonance electromagnetic singularities [START_REF] Van Bladel | Singular Electromagnetic Fields and Sources ͑IEEE[END_REF] ͑lightning rod effect͒. On the other hand, f ͑2͒ is expected to result only from lightning rod effects because no resonance is observable for these particles at such a wavelength due to gold interband transitions. Figure 3͑b͒ shows the influence of the incident polarization on the second harmonic signal. The second harmonic intensity is calculated by fitting each spectrum with a Lorentzian intensity distribution and then integrating it. The second harmonic intensity I ͑2͒ is found to strongly depend on the incident light polarization direction, which indicates that the SHG is resonantly enhanced by LSPR in nanoparticles preferentially excited when the light polarization is oriented along the long axis of the nanorods. Additionally, this polarization allows electromagnetic singularities to be excited at the rod extremities. Given the polycrystalline nature of the samples, which is found to be globally isotropic, the orientational dependence of the frequency doubling cannot be related to crystal structure. The results presented in Fig. 3 suggest the possible influence of plasmon resonance on second harmonic generation from the nanostructures. In Fig. 4, the study of the influence of the irradiation wavelength on the second harmonic generation process confirms the role of the plasmon resonance. Results from excitation spectroscopy of SHG from 150, 170, and 190 nm long axis gold nanorods are presented in Fig. 4. The light polarization used to illuminate the structures and to record the extinction spectra is oriented parallel to the nanorod long axis in order to maximize the SHG signal. It can be observed that whatever the long axis size, the second harmonic signal follows the extinction spectrum ͑solid line͒ of the nanoparticles and thus clearly demonstrates the role of the LSPR in the SHG process. At the half-width, the extinction spectrum peak is broader than the one corresponding to the SHG intensity. This can be explained by the fact that the second harmonic signal is much more sensitive to the field enhancement than extinction. Indeed, nonlinear processes are particularly sensitive to these local resonances due to their quadratic dependence on the intensity. According to Eq. ͑1͒, I ͑2͒ is proportional to the fourth power of the field enhancement at the fundamental frequency, which originates from LSPR excitation. A small variation in the plasmon resonance intensity thus leads to strong variations in second harmonic intensity, as observed in Fig. 4. The wavelength dependence studies may also suggest that the second harmonic signal has a dipolar electric origin rather than a quadripolar electric one. 8 Although the SHG is theoretically forbidden in centrosymmetrical systems, we make the assumption that, in our case, due to the lithographic fabrication process, the nonlinear generation process may arise from a deviation of the shape of the nanoparticles from that of a perfect symmetrical nanorod as well as from the broken symmetry at the air-metal and metalsubstrate interfaces. Defects in the crystalline structure of gold nanoparticles also have to be considered. Finally, it should also be pointed out that due to the large range of wave vectors produced by confined plasmon excitation, depolarization effects can be induced; i.e., vertical component of the near-field appears, making asymmetry discussion nontrivial.

In conclusion, our polarization and spectroscopic studies clearly demonstrate that second harmonic generation from metallic nanoparticles strongly depends on their dipolar plasmon resonance. Resonance enhancement of the second harmonic intensity has been observed while tuning the polarization of the pump beam from parallel to perpendicular to the long axis of nanoparticles. We also observed a strong variation in the wavelength dependence of the second harmonic generation intensity when the irradiation wavelength is tuned towards the extinction peak. This further highlights the influence of the field enhancement from resonance plasmon excitation in the second harmonic generation process from metallic nanoparticles.

  ͑Received 17 January 2007; accepted 4 April 2007͒

b͒

  FIG. 1. ͑Color online͒ ͑a͒ Experimental setup. ͑b͒ Excitation power dependence of the detected SHG signal. The height, width, and length of the gold nanorods were equal to 60, 50, and 150 nm, respectively. Irradiation wavelength was equal to 800 nm and incident polarization was parallel to the nanoparticle long axis. The insert shows a scanning electron microscope image of gold nanorods. X and Y directions are shown with arrows.

FIG. 2 .

 2 FIG. 2. ͑Color online͒ Extinction spectra from arrays of gold nanorods with long axis equal to 150 nm ͑solid curve͒ and 170 nm ͑dashed curve͒. The incident polarization used to record the spectra is schematized by arrows.

  This work was supported by the Region Alsace and by the Centre National de la Recherche Scientifique ͑CNRS͒. Surface Plasmons on Smooth and Rough Surfaces and on Gratings ͑Springer, Berlin, 1988͒, , . ford, 1995͒, , . FIG. 4. ͑Color online͒ Second harmonic generation enhancement ͑circles͒ from arrays of gold nanorods with ͑a͒ 150 nm, ͑b͒ 170 nm, and ͑c͒ 190 nm long axis ͑the dashed line serves as a guide for the eyes͒. The extinction spectrum ͑solid line͒ is shown for comparison. The irradiation time and power used to record the second harmonic signal were equal to 5 s and 50 mW, respectively. The incident polarization was set parallel to the nanoparticle long axis. Au: Pls. check change of "ᮀ" to "." #2 Au: Pls. supply volume & page numbers for Refs. 1, 2, & 16.

	PROOF COPY 108717APL
	1-3	Hubert et al.	Appl. Phys. Lett. 90, 1 ͑2007͒
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H. Raether,