Evaluation Metrics for the Cost of Data Movement in Deep Neural Network Acceleration

Abstract

Hardware accelerators are designed to support a specialized processing dataflow for everchanging deep neural networks (DNNs) under various processing environments. This paper introduces two hardware properties to describe the cost of data movement in each memory hierarchy. Based on the hardware properties, this paper proposes a set of evaluation metrics that are able to evaluate the number of memory accesses and the required memory capacity according to the specialized processing dataflow. Proposed metrics are able to analytically predict energy, throughput, and area of a hardware design without detailed implementation. Once a processing dataflow and constraints of hardware resources are determined, the proposed evaluation metrics quickly quantify the expected hardware benefits, thereby reducing design time.