On the Mitigation of Read Disturbances in Neuromorphic Inference Hardware
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
Funder
National Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Software
Link
https://ieeexplore.ieee.org/ielam/6221038/10066123/9701923-aam.pdf
Reference12 articles.
1. Hardware Fault Tolerance for Binary RRAM Crossbars
2. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding;han;arXiv 1510 00149 [cs],2015
3. Mapping Spiking Neural Networks to Neuromorphic Hardware
4. Neuron Fault Tolerance in Spiking Neural Networks
5. Multilevel HfO2-based RRAM devices for low-power neuromorphic networks
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3. NeuSB: A Scalable Interconnect Architecture for Spiking Neuromorphic Hardware;IEEE Transactions on Emerging Topics in Computing;2023-04-01
4. Design-Technology Co-Optimization for NVM-Based Neuromorphic Processing Elements;ACM Transactions on Embedded Computing Systems;2022-11-30
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