A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2204.04430v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
Computation on a large volume of data at high speed and low power requires energy-efficient computing architectures. Spiking neural network (SNN) with bio-inspired spike-timing-dependent plasticity learning (STDP) is a promising solution for energy-efficient neuromorphic systems than conventional artificial neural network (ANN). Previous works on SNN with STDP learning primarily uses memristive devices which are difficult to fabricate. Some reported works on SNN makes use of memristor macro<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.04430v1">arXiv:2204.04430v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xfrssaacn5celfovendwepp4ni">fatcat:xfrssaacn5celfovendwepp4ni</a> </span>
more »... ls, which are software-based and cannot give complete insight into circuit implementation challenges. This article presents for the first time, a full circuit-level implementation of the SNN system featuring on-chip unsupervised STDP learning in standard CMOS technology. It does not involve the use of FPGAs, CPUs or GPUs for training the neural network. We demonstrated the complete circuit-level design, implementation and simulation of SNN with on-chip training and inference for pattern classification using 180 nm CMOS technology. A comprehensive comparison of the proposed SNN circuit with the previous related work is also presented. To demonstrate the versatility of the CMOS synapse circuit for application scenarios requiring rate-based learning, we have tuned the pair-based STDP circuit to obtain Bienenstock-Cooper-Munro (BCM) characteristics and applied it to heart rate classification.
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