Offline Training-Based Mitigation of IR Drop for ReRAM-Based Deep Neural Network Accelerators

Abstract

Recently, resistive RAM (ReRAM)-based hardware accelerators showed unprecedented performance compared the digital accelerators. Technology scaling causes an inevitable increase in interconnect wire resistance, which leads to IR drops that could limit the performance of ReRAM-based accelerators. These IR drops deteriorate the signal integrity and quality, especially in the crossbar structures which are used to build high-density ReRAMs. Hence, finding a software solution, which can predict the effect of IR drop without involving expensive hardware or SPICE simulations, is very desirable. In this article, we propose two neural networks models to predict the impact of the IR drop problem. These models are used to evaluate the performance of the different deep neural network (DNN) models including binary and quantized neural networks showing similar performance (i.e., recognition accuracy) to the golden validation (i.e., SPICE-based DNN validation). In addition, these predication models are incorporated into the DNN training framework to efficiently retrain the DNN models and bridge the accuracy drop. To further enhance the validation accuracy, we propose incremental training methods. The DNN validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method even with challenging datasets, such as CIFAR10 and SVHN.