Model Inference Optimization on ReRAM-Based Accelerators with Intra- and Inter-OU Similarity

Computing-In-Memory (CIM) technology is promising for DNN inference acceleration, in which ReRAM-based crossbars have received extensive attention in various aspects. However, the current model deployment research often neglects the read-write imbalance problem. This work proposes a compre-hensive solution of inference optimization for multi-bit ReRAM-based crossbar array accelerator. In fine grain of operation unit (OU), a row and column exchange algorithm called RCSwap is proposed to minimize the difference between the weights before and after update and therefore reduce the program time and energy consumption. And in coarse grain, similarity-based scheduling method is presented to exploit the inter-OU difference for further performance improvement. Experiment results show that the proposed methods combined reduce up to 33% and 34% in time delay and energy consumption for model inference, respectively.