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- Centre National de la Recherche ScientifiqueFR152 papers
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6 papers · 1 filter
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…
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…
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
Tifenn Hirtzlin, Bogdan Penkovsky, Jacques-Olivier Klein +5
One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow impr…
Stochastic Computing for Hardware Implementation of Binarized Neural Networks
Tifenn Hirtzlin, Bogdan Penkovsky, Marc Bocquet +3
Binarized Neural Networks, a recently discovered class of neural networks with minimal memory requirements and no reliance on multiplication, are a fantastic opportunity for the re…
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…
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…