Face classification using electronic synapses

Peng Yao, Huaqiang Wu (), Bin Gao, Sukru Burc Eryilmaz, Xueyao Huang, Wenqiang Zhang, Qingtian Zhang, Ning Deng, Luping Shi, H.-S. Philip Wong and He Qian
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Peng Yao: Institute of Microelectronics, Tsinghua University
Huaqiang Wu: Institute of Microelectronics, Tsinghua University
Bin Gao: Institute of Microelectronics, Tsinghua University
Sukru Burc Eryilmaz: Stanford University
Xueyao Huang: Institute of Microelectronics, Tsinghua University
Wenqiang Zhang: Institute of Microelectronics, Tsinghua University
Qingtian Zhang: Institute of Microelectronics, Tsinghua University
Ning Deng: Institute of Microelectronics, Tsinghua University
Luping Shi: Center for Brain-Inspired Computing Research, Tsinghua University
H.-S. Philip Wong: Stanford University
He Qian: Institute of Microelectronics, Tsinghua University

Nature Communications, 2017, vol. 8, issue 1, 1-8

Abstract: Abstract Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.

Date: 2017
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DOI: 10.1038/ncomms15199

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