Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays

Yuhan Shi, Leon Nguyen, Sangheon Oh, Xin Liu, Foroozan Koushan, John R. Jameson and Duygu Kuzum ()
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Yuhan Shi: University of California, San Diego
Leon Nguyen: University of California, San Diego
Sangheon Oh: University of California, San Diego
Xin Liu: University of California, San Diego
Foroozan Koushan: Adesto Technologies Corporation
John R. Jameson: Adesto Technologies Corporation
Duygu Kuzum: University of California, San Diego

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: Abstract Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings.

Date: 2018
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DOI: 10.1038/s41467-018-07682-0

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