Data Movement Reduction for DNN Accelerators: Enabling Dynamic Quantization Through an eFPGA

Abstract

Computational requirements for deep neural networks (DNNs) have been on a rising trend for years. Moreover, network dataflows and topologies are becoming more sophisticated to address more challenging applications. DNN accelerators cannot adopt quickly to the constantly changing DNNs. In this paper, we describe our approach to make a static accelerator more versatile by adding an embedded FPGA (eFPGA). The eFPGA is tightly coupled to the on-chip network, which allows us to pass data through the eFPGA before and after it is processed by the DNN accelerator. Hence, the proposed solution is able to quickly address changing requirements. To show the benefits of this approach, we propose an eFPGA application that enables dynamic quantization of data. We can fit four number converters on an 1.5 mm 2 eFPGA, which can process 400 M data elements per second. We will practically validate our work in the near future, with a SoC tapeout in the ongoing EPI project.