NANOINFER is my newest project! It started in 2017 and is funded by a European Research Council Starting Grant for a duration of 5 years.
I am hiring for this exciting project
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.
D. Bonnet, T. Hirtzlin, A. Majumdar, T. Dalgaty, E. Esmanhotto, V. Meli, N. Castellani, S.Martin, J.-F. Nodin, G. Bourgeois, J.-M. Portal, D. Querlioz, E. Vianello, "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks", Nature Communications 14, 7530 (2023). Open access link
K.-E. Harabi, T. Hirtzlin, C. Turck, E. Vianello, R. Laurent, J. Droulez, P. Bessière, J.-M. Portal, M. Bocquet, D. Querlioz, "A memristor-based Bayesian machine", Nature Electronics, 6, 52 (2023). Link. Preprint.
M. Drouhin, S. Li, M. Grelier, S. Collin, F. Godel, R. G. Elliman, B. Dlubak, J. Trastoy, D. Querlioz, J. Grollier, "Characterization and modeling of spiking and bursting in experimental NbO x neuron", Neuromorphic Computing and Engineering, 2(4), 044008 (2022). Open access link
X. Chen, F. A. Araujo, M. Riou, J. Torrejon, D. Ravelosona, W. Kang, W. Zhao, J. Grollier, D. Querlioz, "Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations", Nature Communications 13, 1016 (2022). Open access link
V. Parmar, B. Penkovsky, D. Querlioz, M. Suri, "Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing", Frontiers in Neuroscience, 15:781786, 2022. Open access link
A. Majumdar, M. Bocquet, T. Hirtzlin, A. Laborieux, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal, D. Querlioz, "Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks", IEEE Transactions on Electron Devices, Vol. 68(10), 4925-4932, 2021. Link. Preprint.
A. Laborieux, M. Ernoult, T. Hirtzlin, D. Querlioz, "Synaptic metaplasticity in binarized neural networks", Nature Communications 12, Article number: 2549, 2021. Open access link.
T. Dalgaty, E. Esmanhotto, N. Castellani, D. Querlioz, E. Vianello, "Ex Situ Transfer of Bayesian Neural Networks to Resistive Memory‐Based Inference Hardware", Advanced Intelligent Systems, Vol. 3(8), 2000103, 2021. Open access link.
A. Laborieux, M. Ernoult, B. Scellier, Y. Bengio, J. Grollier, and D. Querlioz, "Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing Its Gradient Estimator Bias", Frontiers in Neuroscience, Vol. 15, p. 129, 2021. Open access link.
T. Dalgaty, N. Castellani, C. Turck, K.-E. Harabi, D. Querlioz, E. Vianello, "In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling", Nature Electronics, Vol. 4, p. 151, 2021. Link. Preprint.
A. Laborieux, M. Bocquet, T. Hirtzlin, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal, and D. Querlioz, "Implementation of Ternary Weights With Resistive RAM Using a Single Sense Operation per Synapse." IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 68, p. 138, 2021. Link. Preprint.
D. Marković, A. Mizrahi, D. Querlioz, J. Grollier, "Physics for neuromorphic computing", Nature Reviews Physics, Vol. 2, p. 499, 2020. Link. Preprint.
J. Grollier, D. Querlioz, K. Y. Camsari, K. Everschor-Sitte, S. Fukami, M. D. Stiles, "Neuromorphic spintronics", Nature Electronics, Vol. 3, p 360, 2020. Link. Preprint.
T. Hirtzlin, M. Bocquet, B. Penkovskyi, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal and D. Querlioz, "Digital Biologically Plausible Implementation of Binarized Neural Networks with Differential Hafnium Oxide Resistive Memory Arrays", Frontiers in Neuroscience - Neuromorphic Engineering, Vol. 13, p. 1383, 2020. Open access link. Preprint.
T. Hirtzlin, B. Penkovsky, M. Bocquet, J.-O. Klein, J.-M.l Portal and D. Querlioz, "Stochastic Computing for Hardware Implementation of Binarized Neural Networks", IEEE Access, 76394 , 2019. Open access link.
M. Ernoult, J. Grollier and D. Querlioz, "Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines", Scientific Reports, Vol. 9, Article number: 1851 (2019). Open access link.
C. H. Bennett, V. Palmar, L. E. Calvet, J.-O. Klein, M. Suri, M. J. Marinella and D. Querlioz, "Contrasting advantages of learning with random weights and backpropagation in non-volatile memory neural networks," IEEE Access, 73938 , 2019. Open access link.
A. Mizrahi, T. Hirtzlin, A. Fukushima, H. Kubota, S. Yuasa, J. Grollier and D. Querlioz, "Neural-like computing with populations of superparamagnetic basis functions", Nature Communications, Vol. 9, Article number: 1533 (2018). Open access link. Press release
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, and D. Querlioz, "Low-energy truly random number generation with superparamagnetic tunnel junctions for unconventional computing", Physical Review Applied, Vol. 8, 054045, 2017. Link. Preprint . Story in nanotechweb
A. Renaudineau, K. E. Harabi, C. Turck, A. Laborieux, E. Vianello, M. Bocquet, J.-M. Portal, D. Querlioz, "Experimental Demonstration of Memristor Delay-Based Logic In-Memory Ternary Neural Network", 2023 Silicon Nanoelectronics Workshop (SNW) (pp. 43-44)., 2023. Link. Preprint
C. Turck, K. E. Harabi, T. Hirtzlin, E. Vianello, R. Laurent, J. Droulez, P. Bessiere, M. Bocquet, J.-M. Portal, D. Querlioz, "Energy-Efficient Bayesian Inference Using Near-Memory Computation with Memristors", Design, Automation & Test in Europe Conference & Exhibition (DATE), 2023. Link. Preprint.
M. Ezzadeen, A. Majumdar, S. Thomas, J. P. Noël, B. Giraud, M. Bocquet, M., F. Andrieu, D. Querlioz, J. M. Portal, "Binary ReRAM-based BNN first-layer implementation", Design, Automation & Test in Europe Conference & Exhibition (DATE), 2023 . Link. Preprint.
K.-E. Harabi , C. Turck , M. Drouhin , A. Renaudineau , T. Bersani-Veroni , D. Querlioz , T. Hirtzlin , E. Vianello , M Bocquet , J.-M. Portal, "A Multimode Hybrid Memristor-CMOS Prototyping Platform Supporting Digital and Analog Projects", 28th Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 184-185, 2023. Link. Preprint.
J. Laydevant, M. Ernoult, D. Querlioz, J. Grollier, "Training Dynamical Binary Neural Networks With Equilibrium Propagation", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4640-4649, 2021. Open access link.
M. Ezzadeen, A. Majumdar, M. Bocquet, B. Giraud, J.-P. Noël, F. Andrieu, D. Querlioz, J.-M. Portal, "Low-Overhead Implementation of Binarized Neural Networks Employing Robust 2T2R Resistive RAM Bridges", IEEE European Solid State Circuits Conference (ESSCIRC), pp. 83-86, 2021. Link
F. Jebali, A. Majumdar, A. Laborieux, T. Hirtzlin, E. Vianello, J.-P. Walder, M. Bocquet, D. Querlioz, J.-M. Portal, "CAPC: A Configurable Analog Pop-Count Circuit for Near-Memory Binary Neural Networks", 64th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 158-161, 2021. Link
A. Laborieux, M. Bocquet, T. Hirtzlin, J.-O. Klein, L. Herrera-DIez, E. Nowak, E. Vianello, J.-M. Portal and D. Querlioz, "Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse", IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2020.
M. Bocquet, T. Hirtzlin, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal and D. Querlioz, "Embracing the Unreliability of Memory Devices for Neuromorphic Computing ", International Reliability Physics Symposium (IRPS), 2020.
B. Penkovsky, M. Bocquet, T. Hirtzlin, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal and D. Querlioz, "In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications", Design, Automation and Test in Europe Conference (DATE), 2020. (Invited)
T. Hirtzlin, M. Bocquet, M. Ernoult, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal, D. Querlioz, "Hybrid Analog-Digital Learning with Differential RRAM Synapses", IEEE International Electron Devices Meeting (IEDM), 2019. (Invited)
M. Ernoult, J. Grollier, D. Querlioz, Y. Bengio and B. Scellier, "Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input", Conference on Neural Information Processing Systems (NeurIPS), accepted, 2019. Preprint.
D. Querlioz, T. Hirtzlin, J.-O. Klein, E. Nowak, E. Vianello, M. Bocquet, J.-M. Portal, M.Romera, P. Talatchian and J. Grollier, "Memory-Centric Neuromorphic Computing With Nanodevices", IEEE Biomedical Circuits and systems Conference (BIOCAS), 2019. Link.
T. Hirtzlin, B. Penkovsky, J.-O. Klein, N. Locatelli, A. F. Vincent, M. Bocquet, J.-M. Portal and D. Querlioz, "Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction", IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), accepted, 2019. Preprint.
T. Hirtzlin, M. Bocquet, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal, D. Querlioz, "Outstanding Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks", IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), p 288, 2019. Link. Preprint.
M. Bocquet, T. Hirztlin, J.-O. Klein, E. Nowak, E. Vianello, J.-M. Portal and D. Querlioz, "In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks", IEEE International Electron Devices Meeting (IEDM), p. 20.6.1, 2018. Link. Preprint.
N. Locatelli, A. F. Vincent, D. Querlioz, "Use of Magnetoresistive Random-Access Memory As Approximate Memory for Training Neural Networks", Proc. ICECS, p. 553 , 2018. Link. Preprint.
D. Vodenicarevic, N. Locatelli, A. Mizrahi, T. Hirtzlin, J. Friedman, J. Grollier, D. Querlioz, "Circuit-Level Evaluation of the Generation of Truly Random Bits with Superparamagnetic Tunnel Junctions", Proc. ISCAS, 2018. Link. Preprint.