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A 65 nm 12.92-nJ/Inference Mixed-Signal Neuromorphic Processor for Image Classification
Spiking neural networks are a promising candidate for next-generation machine learning and are suitable for power-constrained edge devices. In this brief, we present a mixed-signal neuromorphic processor that efficiently implements an echo state network (ESN) and achieves high accuracy without a cos...
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Published in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2023-08, Vol.70 (8), p.2804-2808 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Spiking neural networks are a promising candidate for next-generation machine learning and are suitable for power-constrained edge devices. In this brief, we present a mixed-signal neuromorphic processor that efficiently implements an echo state network (ESN) and achieves high accuracy without a costly on-chip training process. The design employs a charge-domain computation circuit that efficiently realizes a leaky integrate and fire neuron. Combined with optimizing sparse connections, the proposed algorithm-hardware co-design approach results in a highly energy-efficient operation while delivering high accuracy. Fabricated in a 65nm LP process, the processor is measured to achieve 95.35% MNIST classification accuracy, which closely matches the software model, and energy efficiency of 12.92nJ per classification. |
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ISSN: | 1549-7747 1558-3791 |
DOI: | 10.1109/TCSII.2023.3252501 |