A 65 nm 12.92-nJ/Inference Mixed-Signal Neuromorphic Processor for Image Classification
Yejun Ko, Sunghoon Kim, Kwanghyun Shin, Youngmin Park, Sundo Kim, Dongsuk Jeon · IEEE Transactions on Circuits & Systems II Express Briefs · 2023
Spiking neural networks are a promising candidate for next-generation machine learning and are suitable for powerconstrained edge devices. In this paper, we present a mixed-signal neuromorphic processor that efficienctly 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.