most citedSpiking Neural Networks Hardware Implementations and Challenges: a Survey

258 citations · 342 across the 7 of their papers we have counts for

collaborators

8 papers

cs.ET2020

Embracing the Unreliability of Memory Devices for Neuromorphic Computing

Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein +4

The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near- or in-memory. However, this type of computation is not compatible with c…

eess.SP2020

In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications

Bogdan Penkovsky, Marc Bocquet, Tifenn Hirtzlin +5

The advent of deep learning has considerably accelerated machine learning development. The deployment of deep neural networks at the edge is however limited by their high memory an…

cs.ET2020

Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse

Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +6

The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a major lead for reducing the energy consum…

cs.NE2020258 cited

Spiking Neural Networks Hardware Implementations and Challenges: a Survey

Maxence Bouvier, Alexandre Valentian, Thomas Mesquida +4

Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing c…

cs.ET20201 cited

In-situ learning harnessing intrinsic resistive memory variability through Markov Chain Monte Carlo Sampling

Thomas Dalgaty, Niccolo Castellani, Damien Querlioz +1

Resistive memory technologies promise to be a key component in unlocking the next generation of intelligent in-memory computing systems that can act and learn locally at the edge.…

cs.ET2019

Digital Biologically Plausible Implementation of Binarized Neural Networks with Differential Hafnium Oxide Resistive Memory Arrays

Tifenn Hirtzlin, Marc Bocquet, Bogdan Penkovsky +5

The brain performs intelligent tasks with extremely low energy consumption. This work takes inspiration from two strategies used by the brain to achieve this energy efficiency: the…