A Mixed Signal 65nm CMOS Implementation of a Spiking Neural Network
Abstract
Spiking neural networks (SNNs) are an emerging class of biologically inspired Artificial Neural
Networks implemented in machine learning and artificial intelligence. Current state-of-the-art
small- and large-scale SNNs are mainly implemented as digital hardware with time-multiplexing
techniques to achieve power efficiency. In this thesis, a 65 nm CMOS mixed signal
asynchronous SNN implementation was designed and simulated. The proposed design reduces
hardware and timing complexity over existing implementations and opens opportunities for
further larger-scale implementations.
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Cite this version of the work
Yangtian Yan
(2022).
A Mixed Signal 65nm CMOS Implementation of a Spiking Neural Network. UWSpace.
http://hdl.handle.net/10012/18648
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