An energy-efficient and high-throughput bitwise CNN on sneak-path-free digital ReRAM crossbar

Abstract

Convolutional neural network (CNN) based machine learning requires a highly parallel as well as low power consumption (including leakage power) hardware accelerator. In this paper, we will present a digital ReRAM crossbar based CNN accelerator that can achieve significantly higher throughput and lower power consumption than state-of-arts. The CNN is trained with binary constraints on both weights and activations such that all operations become bitwise. With further use of 1-bit comparator, the bitwise CNN model can be naturally realized on a digital ReRAM-crossbar device. A novel sneak-path-free ReRAM-crossbar is further utilized for large-scale realization. Simulation experiments show that the bitwise CNN accelerator on the digital ReRAM crossbar achieves 98.3% and 91.4% accuracy on MNIST and CIFAR-10 benchmarks, respectively. Moreover, it has a peak throughput of 792GOPS at the power consumption of 6.3mW, which is 18.86 times higher throughput and 44.1 times lower power than CMOS CNN (non-binary) accelerators.