An All-Digital Compute-In-Memory FPGA Architecture for Deep Learning Acceleration

Abstract

Field Programmable Gate Array (FPGA) is a versatile and programmable hardware platform, which makes it a promising candidate for accelerating Deep Neural Networks (DNNs). However, FPGA’s computing energy efficiency is low due to the domination of energy consumption by interconnect data movement. In this paper, we propose an all-digital Compute-In-Memory FPGA architecture for deep learning acceleration. Furthermore, we present a bit-serial computing circuit of the Digital CIM core for accelerating vector-matrix multiplication (VMM) operations. A Network-CIM-Deployer (NCIMD) is also developed to support automatic deployment and mapping of DNN networks. NCIMD provides a user-friendly API of DNN models in Caffe format. Meanwhile, we introduce a Weight-Stationary (WS) dataflow and describe the method of mapping a single layer of the network to the CIM array in the architecture. We conduct experimental tests on the proposed FPGA architecture in the field of Deep Learning (DL), as well as in non-DL fields, using different architectural layouts and mapping strategies. We also compare the results with the conventional FPGA architecture. The experimental results show that compared to the conventional FPGA architecture, the energy efficiency can achieve a maximum speedup of 16.1 ×, while the latency can decrease up to \(40\% \) in our proposed CIM FPGA architecture.