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20192021
most citedIn-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks

83 citations · 89 across the 6 of their papers we have counts for

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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…

cs.ET2020

Implementation of Ternary Weights with Resistive RAM Using a Single Sense Operation per Synapse

Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +5

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

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.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…

cs.ET2019

Outstanding Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks

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

Resistive random access memories (RRAM) are novel nonvolatile memory technologies, which can be embedded at the core of CMOS, and which could be ideal for the in-memory implementat…

cs.ET201983 cited

In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks

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

RRAM-based in-Memory Computing is an exciting road for implementing highly energy efficient neural networks. This vision is however challenged by RRAM variability, as the efficient…