XoMA: Exclusive on-chip memory architecture for energy-efficient deep learning acceleration

Abstract

State-of-the-art deep neural networks (DNNs) require hundreds of millions of multiply-accumulate (MAC) computations to perform inference, e.g. in image-recognition tasks. To improve the performance and energy efficiency, deep learning accelerators have been proposed, realized both on FPGAs and as custom ASICs. Generally, such accelerators comprise many parallel processing elements, capable of executing large numbers of concurrent MAC operations. From the energy perspective, however, most consumption arises due to memory accesses, both to off-chip external memory, and on-chip buffers. In this paper, we propose an on-chip DNN co-processor architecture where minimizing memory accesses is the primary design objective. To the maximum possible extent, off-chip memory accesses are eliminated, providing lowest-possible energy consumption for inference. Compared to a state-of-the-art ASIC, our architecture requires 36% fewer external memory accesses and 53% less energy consumption for low-latency image classification.