A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case Study

Abstract

General Matrix to Matrix multiplication (GEMM) is the cornerstone for a wide gamut of applications in high performance computing (HPC), scientific computing (SC) and more recently, deep learning. In this work, we present a customizable matrix multiplication framework for the Intel HARPv2 CPU+FPGA platform that includes support for both traditional single precision floating point and reduced precision workloads. Our framework supports arbitrary size GEMMs and consists of two parts: (1) a simple application programming interface (API) for easy configuration and integration into existing software and (2) a highly customizable hardware template. The API provides both compile and runtime options for controlling key aspects of the hardware template including dynamic precision switching; interleaving and block size control; and fused deep learning specific operations. The framework currently supports single precision floating point (FP32), 16, 8, 4 and 2 bit Integer and Fixed Point (INT16, INT8, INT4, INT2) and more exotic data types for deep learning workloads: INT16xTernary, INT8xTernary, BinaryxBinary. We compare our implementation to the latest NVIDIA Pascal GPU and evaluate the performance benefits provided by optimizations built into the hardware template. Using three neural networks (AlexNet, VGGNet and ResNet) we illustrate that reduced precision representations such as binary achieve the best performance, and that the HARPv2 enables fine-grained partitioning of computations over both the Xeon and FPGA. We observe up to 50x improvement in execution time compared to single precision floating point, and that runtime configuration options can improve the efficiency of certain layers in AlexNet up to 4x, achieving an overall 1.3x improvement over the entire network.