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The Artificial Intelligence (AI) disruption continues unabated, albeit at extreme compute requirements. Neuromorphic circuits and systems offer a panacea for this extravagance. To this effect, event-based learning such as spike-timing-dependent plasticity (STDP) in spiking neural networks (SNNs) is an active area of research. Hebbian learning in SNNs fundamentally involves synaptic weight updates based on temporal correlations between pre- and post- synaptic neural activities. While there are broadly two approaches of realizing STDP, i.e. All-to-All versus Nearest Neighbor (NN), there exist strong arguments favoring the NN approach on the biologically plausibility front. In this paper, we present a novel current-mode implementation of a postsynaptic event-based NN STDP-based synapse. We leverage transistor subthreshold dynamics to generate exponential STDP traces using repurposed log-domain low-pass filter circuits. Synaptic weight operations involving addition and multiplications are achieved by the Kirchoff current law and the translinear principle respectively. Simulation results from the NCSU TSMC 180 nm technology are presented. Finally, the ideas presented here hold implications for engineering efficient hardware to meet the growing AI training and inference demands.
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Deep unsupervised learning using spike-timing-dependent plasticity
Abstract Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for spiking neural networks that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve 24.56% higher accuracy and 3.5 × faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to ak-means clustering approach.
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- Award ID(s):
- 1955815
- PAR ID:
- 10507557
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Neuromorphic Computing and Engineering
- Volume:
- 4
- Issue:
- 2
- ISSN:
- 2634-4386
- Page Range / eLocation ID:
- 024004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1088/2634-4386/ad3a95
