Single-Channel Dataflow for Convolutional Neural Network Accelerator

Abstract

Convolutional Neural Networks (CNNs) are popular in Machine Learning and CNNs have rich parallelism. Many efficient CNN accelerators have been designed recently. Especially, the Tiling structure accelerators are widely used. However, we observe that the Tiling architecture may not be efficient when the network layers change. This situation will result in a waste of processing elements (PEs). In order to achieve a high-utilization of PEs, we propose a new architecture called Single-Channel to 1) ameliorate the Tiling architecture without increasing the hardware complexity while 2) improve the utilization of PEs. We evaluate the Single-Channel architecture with four typical CNN networks. The hardware achieves the 1.2-5× speedup and the 40%-60% utilization improvement compared with the Tiling architectures.