Adapt-Flow: A Flexible DNN Accelerator Architecture for Heterogeneous Dataflow Implementation

Abstract

Deep neural networks (DNNs) have been widely applied to various application domains. DNN computation is memory and compute-intensive requiring excessive memory access and a large number of computations. To efficiently implement these applications, several data reuse and parallelism exploitation strategies, called dataflows, have been proposed. Studies have shown that many DNN applications benefit from a heterogeneous dataflow strategy where the dataflow type changes from layer to layer. Unfortunately, very few existing DNN architectures can simultaneously accommodate multiple dataflows due to their limited hardware flexibility. In this paper, we propose a flexible DNN accelerator architecture, called Adapt-Flow, which has the capability of supporting multiple dataflow selections for each DNN layer at runtime. Specifically, the proposed Adapt-Flow architecture consists of (1) a flexible interconnect, (2) a dataflow selection algorithm, and (3) a dataflow mapping technique. The flexible interconnect provides dynamic support for various traffic patterns required by different dataflows. The proposed dataflow selection algorithm selects the optimal dataflow strategy for a given DNN layer with the aim of much improved performance. And the dataflow mapping technique efficiently maps the dataflow amenable to the flexible interconnect. Simulation studies show that the proposed Adapt-Flow architecture reduces execution time by 46%, 78%, 26%, and energy consumption by 45%, 80%, 25% as compared to NVDLA, ShiDianNao, and Eyeriss respectively.