Towards an efficient dataflow-flexible accelerator by finding optimal dataflows of DNNs

Abstract

This paper proposes a new dataflow-flexible accelerator design that addresses the limitations of existing heterogeneous dataflow accelerator (HDA) for handling the computation of multiple deep neural network (DNN) models. The design offers increased dataflow flexibility and higher efficiency compared to existing works. The accelerator utilizes a fixed set of representative dataflows implemented as operating modes and switches between them dynamically. A design space exploration (DSE) tool is leveraged to evaluate the efficiency of candidate dataflows and determine the optimal number and types of operating modes. Each layer of the target DNN models is assessed with different operating modes to select the optimal mode for each layer. Also, two supplementary optimization techniques are adopted to reduce the overheads from supporting a multitude of dataflows. One optimizes to minimize the number of transitions of dataflows, which incur severe overheads. The other optimizes to maximize the reuse of hardware components associated with supporting multiple dataflows. By identifying the redundant hardware components, the proposed design minimizes the chip area, another aspect where dataflow-flexible accelerators suffer. Experimental results demonstrate that our algorithm achieves greater dataflow flexibility with high efficiency, Compared to HDA, our design is, on average, 34.6 % lower in latency at the cost of 6.4 % area and negligible energy overhead.