Project description
New ways to integrate emerging memories to enable neuromorphic computing systems
Artificial intelligence (AI) and machine learning are used today for computing all kinds of data, making predictions and solving problems. These are processes based increasingly on deep neuronal network (DNN) models. As the volume of produced data slow down machines and consume greater amounts of energy, there is a new generation of neural units. The spiking neural networks (SNNs) incorporate biologically-feasible spiking neurons with their temporal dynamics. The EU-funded TEMPO project will leverage emerging memory technology to design new innovative technological solutions that make data integration simpler and easier via new neuronal DNN and SNN computing engines. Reduced core computational operational systems’ neuromorphic algorithms will serve as demonstrators.
Objective
Massive adoption of computing in all aspects of human activity has led to unprecedented growth in the amount of data generated. Machine learning has been employed to classify and infer patterns from this abundance of raw data, at various levels of abstraction. Among the algorithms used, brain-inspired, or “neuromorphic”, computation provides a wide range of classification and/or prediction tools. Additionally, certain implementations come about with a significant promise of energy efficiency: highly optimized Deep Neural Network (DNN) engines, ranging up to the efficiency promise of exploratory Spiking Neural Networks (SNN). Given the slowdown of silicon-only scaling, it is important to extend the roadmap of neuromorphic implementations by leveraging fitting technology innovations. Along these lines, the current project aims to sweep technology options, covering emerging memories and 3D integration, and attempt to pair them with contemporary (DNN) and exploratory (SNN) neuromorphic computing paradigms. The process- and design-compatibility of each technology option will be assessed with respect to established integration practices. Core computational kernels of such DNN/SNN algorithms (e.g. dot-product/integrate-and-fire engines) will be reduced to practice in representative demonstrators.
Fields of science
Keywords
Programme(s)
Call for proposal
H2020-ECSEL-2018-2-RIA-two-stage
See other projects for this callSub call
H2020-ECSEL-2018-2-RIA-two-stage-1
Funding Scheme
RIA - Research and Innovation actionCoordinator
3001 Leuven
Belgium
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Participants (19)
75015 PARIS 15
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80686 Munchen
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5656 AE Eindhoven
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Participation ended
94000 CRETEIL
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38920 Crolles
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31100 Toulouse
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5656 AG Eindhoven
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38000 Grenoble
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5617BA Eindhoven
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
01069 Dresden
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70839 Gerlingen-Schillerhoehe
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
72770 Reutlingen
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85579 Neubiberg
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97499 Donnersdorf
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8006 Zurich
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74321 Bietigheim Bissingen
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8050 Zurich
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
5684 PC Best
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30165 Hannover
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.