Conductance-Aware Quantization Based on Minimum Error Substitution for Non-Linear-Conductance-State Tolerance in Neural Computing Systems

Emerging resistive random-access memory (ReRAM) has demonstrated great potential in the achievement of the in-memory computing paradigm to overcome the well-known "memory wall" in current von Neumann architecture. The ReRAM crossbar array (RCA) is a promising circuit structure to accelerate the vital multiplication-and-accumulation (MAC) operations in deep neural networks (DNN). However, due to the nonlinear distribution of conductance levels in ReRAM, a large deviation exists in the mapping process when the trained weights that are quantized by linear relationships are directly mapped to the nonlinear conductance values from the realistic ReRAM device. This deviation degrades the inference accuracy of the RCA-based DNN. In this paper, we propose a minimum error substitution based on a conductance-aware quantization method to eliminate the deviation in the mapping process from the weights to the actual conductance values. The method is suitable for multiple ReRAM devices with different non-linear conductance distribution and is also immune to the device variation. The simulation results on LeNet5, AlexNet and VGG16 demonstrate that this method can vastly rescue the accuracy degradation from the non-linear resistance distribution of ReRAM devices compared to the linear quantization method.