Fully-Integrated Spiking Neural Network using SiOx-based RRAM as synaptic device
2020 2ND IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2020)(2020)
摘要
This paper presents, to the best of the authors' knowledge, the first complete integration of a Spiking Neural Network combining analog neurons and SiOx-based resistive memory (RRAM) synapses. The implemented topology is a perceptron, and the circuit is aimed at performing MNIST digits classification. An existing framework was adapted for off-line learning and weight quantization, and the network was later converted into its spiking equivalent. The test chip, fabricated in 130 nm CMOS, shows a classification accuracy of 82%, with a 180 pJ energy dissipation per spike.
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关键词
silicon oxide, resistive memories, spiking neural network, neuromorphic computing
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