Reducing the Performance Gap between Soft Scalar CPUs and Custom Hardware with TILT

Abstract

By using resource sharing field-programmable gate array (FPGA) compute engines, we can reduce the performance gap between soft scalar CPUs and resource-intensive custom datapath designs. This article demonstrates that Thread- and Instruction-Level parallel Template architecture (TILT), a programmable FPGA-based horizontally microcoded compute engine designed to highly utilize floating point (FP) functional units (FUs), can improve significantly the average throughput of eight FP-intensive applications compared to a soft scalar CPU (similar to a FP-extended Nios). For eight benchmark applications, we show that: (i) a base TILT configuration having a single instance for each FU type can improve the performance over a soft scalar CPU by 15.8 × , while requiring on average 26% of the custom datapaths’ area; (ii) selectively increasing the number of FUs can more than double TILT’s average throughput, reducing the custom-datapath-throughput-gap from 576 × to 14 × ; and (iii) replicated instances of the most computationally dense TILT configuration that fit within the area of each custom datapath design can reduce the gap to 8.27 × , while replicated instances of application-tuned configurations of TILT can reduce the custom-datapath-throughput-gap to an average of 5.22 × , and up to 3.41 × for the Matrix Multiply benchmark. Last, we present methods for design space reduction, and we correctly predict the computationally densest design for seven out of eight benchmarks.