ReGAN: A pipelined ReRAM-based accelerator for generative adversarial networks

Abstract

Generative Adversarial Networks (GANs) have recently drawn tremendous attention in many artificial intelligence (AI) applications including computer vision, speech recognition, and natural language processing. While GANs deliver state-of-the-art performance on these AI tasks, it comes at the cost of high computational complexity. Although recent progress demonstrated the promise of using ReRMA-based Process-In-Memory for acceleration of convolutional neural networks (CNNs) with low energy cost, the unique training process required by GANs makes them difficult to run on existing neural network acceleration platforms: two competing networks are simultaneously co-trained in GANs, and hence, significantly increasing the need of memory and computation resources. In this work, we propose ReGAN - a novel ReRAM-based Process-In-Memory accelerator that can efficiently reduce off-chip memory accesses. Moreover, ReGAN greatly increases system throughput by pipelining the layer-wise computation. Two techniques, namely, Spatial Parallelism and Computation Sharing are particularly proposed to further enhance training efficiency of GANs. Our experimental results show that ReGAN can achieve 240× performance speedup compared to GPU platform averagely, with an average energy saving of 94×.