Low Power In-Memory Implementation of Ternary Neural Networks with ...
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In this work, we revisit one of these architectures where synapses are implemented in a differential fashion to reduce bit errors, and synaptic weights are read ...
May 5, 2020 · In this work, we revisit one of these architectures where synapses are implemented in a differential fashion to reduce bit errors, and synaptic ...
Fast, non-volatile memory that can be embedded at the core of CMOS. • Memory state is the electrical resistance of the device (high or low).
The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a significant lead for ...
May 5, 2020 · Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse · Axel Laborieux, M. Bocquet, +6 authors. D.
Low power in-memory implementation of ternary neural networks with resistive RAM-based synapse. Published in AICAS, 2020. Recommended citation: Laborieux ...
Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse. Axel Laborieux (1) , Marc Bocquet (2) , Tifenn Hirtzlin (1) ...
This project will deepen our understanding of novel memory technologies and develop a toolbox for creating intelligent memory chips.
Oct 26, 2020 · In this work, we focus on the case of ternary neural networks, where synaptic weights assume ternary values. We propose a two-transistor/two- ...
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Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse · Computer Science, Engineering. 2020 2nd IEEE International ...