Reliability-Driven Neuromorphic Computing Systems Design
Qi Xu1,a, Junpeng Wang1,b, Hao Geng2, Song Chen1,c and Xiaoqing Wen3
1School of Microelectronics, University of Science and Technology of China
axuqi@ustc.edu.cn
bwjp97@mailustc.edu.cn
csongch@ustc.edu.cn
2Department of Computer Science and Engineering, The Chinese University of Hong Kong
hgeng@cse.cuhk.edu.hk
3Department of Computer Science and Networks, Kyushu Institute of Technology
wen@cse.kyutech.ac.jp
ABSTRACT
In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have provided a promising solution to the acceleration of neural networks. However, stuck-at faults (SAFs) in the memristor devices significantly degrade the computing accuracy of NCS. Besides, memristors suffer from process variations, causing the deviation of actual programming resistance from its target resistance. In this paper, we propose a novel reliability-driven design framework for a memristive crossbar-based NCS in combination with general and chip-specific design optimizations. First, we design a general reliability-aware training scheme to enhance the robustness of NCS to SAFs and device variations; a dropout-inspired approach is developed to alleviate the impact of SAFs; a new weighted error function, including cross-entropy error (CEE), the l2-norm of weights, and the sum of squares of first-order derivatives of CEE with respect to weights, is proposed to obtain a smooth error curve, where the effects of variations are suppressed. Second, given the neural network model generated by the reliability-aware training scheme, we exploit chip-specific mapping and retraining to further reduce the computation accuracy loss incurred by SAFs. Experimental results clearly demonstrate that the proposed method can boost the computation accuracy of NCS and improve the NCS robustness.
