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NANOINFER SIGNED

Intelligent Memories that Perform Inference with the Physics of Nanodevices

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Project "NANOINFER" data sheet

The following table provides information about the project.

Coordinator
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS 

Organization address
address: RUE MICHEL ANGE 3
city: PARIS
postcode: 75794
website: www.cnrs.fr

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 1˙499˙609 €
 EC max contribution 1˙499˙609 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-STG
 Funding Scheme ERC-STG
 Starting year 2017
 Duration (year-month-day) from 2017-03-01   to  2022-02-28

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS FR (PARIS) coordinator 1˙499˙609.00

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 Project objective

Cognitive tasks are increasingly necessary in modern electronics. The energy efficiency of associated algorithms, which rely on abundant stored parameters, is severely limited by the separation of computation and memory elements in conventional computers. In NANOINFER, I will directly address this challenge by developing intelligent memory chips that natively perform both memory and computing functions, using CMOS and emerging nanodevices. These chips will perform modern Bayesian inference algorithms, which allow cognitive-type reasoning. The project includes theoretical investigations as well as intelligent memory chip designs, which will be supported by proof-of-concept experimental demonstrations. The proposed architectures, based on spintronic and memristive memories, will maximize energy efficiency by leveraging the complex physics of these emerging devices for inference operations and the storage of model parameters, and by minimizing exchanges between computation units and memory. Inference will be performed using sampling algorithms that allow tackling difficult problems and are robust to nanodevice imperfections. The inference circuits will be composed of digital CMOS logic as well as spiking neurons circuits. Two standard Bayesian approaches will be employed to enable learning, permitting highly adaptive systems. Preliminary results on a system that performs naïve Bayesian inference have validated this concept and its use with novel memory technologies. NANOINFER will resolve critical interdisciplinary challenges to permit intelligent memories to perform non-naïve tasks, ensuring a correspondence between device physics and Bayesian concepts while maintaining a fusion between computation and memory. This project will deepen our understanding of novel memory technologies and develop a toolbox for creating intelligent memory chips. These will allow smart devices to perform cognitive/sensory-motor tasks at low energy without requiring large computing machines.

 Publications

year authors and title journal last update
List of publications.
2019 Tifenn Hirtzlin, Bogdan Penkovsky, Jacques-Olivier Klein, Nicolas Locatelli, Adrien F. Vincent, Marc Bocquet, Jean-Michel Portal, Damien Querlioz
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
published pages: , ISSN: , DOI:
15th IEEE / ACM International Symposium on Nanoscale Architectures 2019-10-29
2019 Christopher H. Bennett, Vivek Parmar, Laurie E. Calvet, Jacques-Olivier Klein, Manan Suri, Matthew J. Marinella, Damien Querlioz
Contrasting Advantages of Learning With Random Weights and Backpropagation in Non-Volatile Memory Neural Networks
published pages: 73938-73953, ISSN: 2169-3536, DOI: 10.1109/access.2019.2920076
IEEE Access 7 2019-08-29
2019 Maxence Ernoult, Julie Grollier, Damien Querlioz
Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines
published pages: , ISSN: 2045-2322, DOI: 10.1038/s41598-018-38181-3
Scientific Reports 9/1 2019-08-29
2019 Tifenn Hirtzlin, Bogdan Penkovsky, Marc Bocquet, Jacques-Olivier Klein, Jean-Michel Portal, Damien Querlioz
Stochastic Computing for Hardware Implementation of Binarized Neural Networks
published pages: 76394-76403, ISSN: 2169-3536, DOI: 10.1109/access.2019.2921104
IEEE Access 7 2019-08-29
2017 D. Vodenicarevic, N. Locatelli, A. Mizrahi, J. S. Friedman, A. F. Vincent, M. Romera, A. Fukushima, K. Yakushiji, H. Kubota, S. Yuasa, S. Tiwari, J. Grollier, D. Querlioz
Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing
published pages: , ISSN: 2331-7019, DOI: 10.1103/physrevapplied.8.054045
Physical Review Applied 8/5 2019-03-11
2018 Alice Mizrahi, Tifenn Hirtzlin, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Julie Grollier, Damien Querlioz
Neural-like computing with populations of superparamagnetic basis functions
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-018-03963-w
Nature Communications 9/1 2019-03-11

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