SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks

Spiking Neural Networks (SNNs) are an active research domain towards energy efficient machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs use temporal spike data and bio-plausible neuronal activation functions such as Leaky-Integrate Fire/Integrate Fire (LIF/IF) fo...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems Jg. 42; H. 11; S. 1
Hauptverfasser:	Moitra, Abhishek, Bhattacharjee, Abhiroop, Kuang, Runcong, Krishnan, Gokul, Cao, Yu, Panda, Priyadarshini
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Analog Crossbars Artificial neural networks Benchmark testing Biological neural networks Computation Computer architecture Computer memory Computer Science Data communication Data processing Emerging Devices Energy efficiency Engineering Engines Hardware In-Memory Computing Modules Network latency Neural networks Neurons Spiking Neural Networks (SNNs)
ISSN:	0278-0070, 1937-4151
Online-Zugang:	Volltext
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