AxOSpike: Spiking Neural Networks-Driven Approximate Operator Design

Abstract

Approximate computing (AxC) is being widely researched as a viable approach to deploying compute-intensive artificial intelligence (AI) applications on resource-constrained embedded systems. In general, AxC aims to provide disproportionate gains in system-level power-performance-area (PPA) by leveraging the implicit error tolerance of an application. One of the more widely used methods in AxC involves circuit pruning of arithmetic operators used to process AI workloads. However, most related works adopt an application-agnostic approach to operator modeling for the design space exploration (DSE) of Approximate Operators (AxOs). To this end, we propose an application-driven approach to designing AxOs. Specifically, we use spiking neural network (SNN)-based inference to present an application-driven operator model resulting in AxOs with better-PPA-accuracy tradeoffs compared to traditional circuit pruning. Additionally, we present a novel FPGA-specific operator model to improve the quality of AxOs that can be obtained using circuit pruning. With the proposed methods, we report designs with up to 26.5% lower PDPxLUTs with similar application-level accuracy. Further, we report a considerably better set of design points than related works with up to 51% better-Pareto front hypervolume.