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The ending of Moore's Law makes domain-specific architecture as the future of computing. The most representative is the emergence of various deep learning accelerators. Among the proposed solutions, resistive random access memory (ReRAM) based process-in-memory (PIM) architecture is anticipated as a promising candidate because ReRAM has the capability of both data storage and in-situ computation. However, we found that existing solutions are unable to efficiently support the computational needs required by the training of unsupervised generative adversarial networks (GANs), due to the lack of the following two features: 1) Computation efficiency: GAN utilizes a new operator, called transposed convolution. It inserts massive zeros in its input before a convolution operation, resulting in significant resource under-utilization; 2) Data traffic: The data intensive training process of GANs often incurs structural heavy data traffic as well as frequent massive data swaps. Our research follows the PIM strategy by leveraging the energy-efficiency of ReRAM arrays for vector-matrix multiplication to enhance the performance and energy efficiency. Specifically, we propose a novel computation deformation technique that can skip zero-insertions in transposed convolution for computation efficiency improvement. Moreover, we explore an efficient pipelined training procedure to reduce on-chip memory access. The implementation of related circuits and architecture is also discussed. At the end, we present our perspective on the future trend and opportunities of deep learning accelerators.

Efficient Process-in-Memory Architecture Design for Unsupervised GAN-based Deep Learning using ReRAM