An Efficient and Accurate Memristive Memory for Array-Based Spiking Neural Networks

Author:

Das Hritom1ORCID,Febbo Rocco D.1,Tushar S. N. B.1,Chakraborty Nishith N.1,Liehr Maximilian2,Cady Nathaniel C.2ORCID,Rose Garrett S.1ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA

2. Department of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA

Funder

Air Force Research Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Universal Configuration Framework for Mem-Element-Emulator-Based Bionic Firing Circuits;IEEE Transactions on Circuits and Systems I: Regular Papers;2024-09

2. Hardware software co-design for leveraging STDP in a memristive neuroprocessor;Neuromorphic Computing and Engineering;2024-05-22

3. HfO2-Based Synaptic Spiking Neural Network Evaluation to Optimize Design and Testing Cost;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

4. Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks;Scientific Reports;2024-04-17

5. Optimizations for a Current-Controlled Memristor- Based Neuromorphic Synapse Design;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2023-12

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