Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding-Reference-Cited by-同舟云学术

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Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding

Published:2016-01-06 Issue: Volume:9 Page:
ISSN:1662-453X
Container-title:Frontiers in Neuroscience
language:
Short-container-title:Front. Neurosci.

Author:

Agarwal Sapan,Quach Tu-Thach,Parekh Ojas,Hsia Alexander H.,DeBenedictis Erik P.,James Conrad D.,Marinella Matthew J.,Aimone James B.

Funder

Sandia National Laboratories

Publisher

Frontiers Media SA

Subject

General Neuroscience

Reference36 articles.

1. Simple, efficient, and neural algorithms for sparse coding.;Arora;arXiv preprint arXiv:,2015

2. Experimental demonstration and tolerancing of a large-scale neural network (165 000 Synapses) using phase-change memory as the synaptic weight element.;Burr;IEEE Trans. Electron Dev.,2015

3. Real-time scalable cortical computing at 46 giga-synaptic OPS/watt with ~100 × Speedup in Time-to-Solution and ~100,000 × reduction in energy-to-solution;Cassidy,2014

4. A comprehensive crossbar array model with solutions for line resistance and nonlinear device characteristics.;Chen;IEEE Trans. Electron Dev.,2013

5. Towards high-speed, write-disturb tolerant 3D vertical RRAM arrays;Chen,2014

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