Approximate Computing: Making Mobile Systems More Efficient

Abstract

Approximate systems can reclaim energy that's currently lost to the "correctness tax" imposed by traditional safety margins designed to prevent worst-case scenarios. Researchers at the University of Washington have co-designed programming language extensions, a compiler, and a hardware co-processor to support approximate acceleration. Their end-to-end system includes two building blocks. First, a new programmer-guided compiler framework transforms programs to use approximation in a controlled way. An Approximate C Compiler for Energy and Performance Tradeoffs (Accept) uses programmer annotations, static analysis, and dynamic profiling to find parts of a program that are amenable to approximation. Second, the compiler targets a system on a chip (SoC) augmented with a co-processor that can efficiently evaluate coarse regions of approximate code. A Systolic Neural Network Accelerator in Programmable logic (Snnap) is a hardware accelerator prototype that can efficiently evaluate approximate regions of code in a general-purpose program.