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Energy-efficient Mott activation neuron for full-hardware implementation of neural networks
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Energy-efficient Mott activation neuron for full-hardware implementation of neural networks
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Journal Article
Energy-efficient Mott activation neuron for full-hardware implementation of neural networks
2021
Overview
To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.
Energy- and area-efficient vanadium-dioxide-based Mott activation neuron devices enable the implementation of activation functions in neural networks.
Publisher
Nature Publishing Group UK,Nature Publishing Group
