Explore the words cloud of the NANOINFER project. It provides you a very rough idea of what is the project "NANOINFER" about.
The following table provides information about the project.
Coordinator |
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Organization address contact info |
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 |
Take a look of project's partnership.
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1 | CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS | FR (PARIS) | coordinator | 1˙499˙609.00 |
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.
year | authors and title | journal | last update |
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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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The information about "NANOINFER" are provided by the European Opendata Portal: CORDIS opendata.