Rescuing RRAM-Based Computing From Static and Dynamic Faults

Emerging resistive random access memory (RRAM) has shown the great potential of in-memory processing capability, and thus attracts considerable research interests in accelerating memory-intensive applications, such as neural networks (NNs). However, the accuracy of RRAM-based NN computing can degrade significantly, due to the intrinsic statistical variations of the resistance of RRAM cells. In this article, we propose SIGHT, a synergistic algorithm-architecture fault-tolerant framework, to holistically address this issue. Specifically, we consider three major types of faults for RRAM computing: 1) nonlinear resistance distribution; 2) static variation; and 3) dynamic variation. From the algorithm level, we propose a resistance-aware quantization to compel the NN parameters to follow the exact nonlinear resistance distribution as RRAM, and introduce an input regulation technique to compensate for RRAM variations. We also propose a selective weight refreshing scheme to address the dynamic variation issue that occurs at runtime. From the architecture level, we propose a general and low-cost architecture accordingly for supporting our fault-tolerant scheme. Our evaluation demonstrates almost no accuracy loss for our three fault-tolerant algorithms, and the proposed SIGHT architecture incurs performance overhead as little as 7.14%.