A Taxonomy of General Purpose Approximate Computing Techniques

Approximate computing is the idea that systems can gain performance and energy efficiency if they expend less effort on producing a “perfect” answer. Approximate computing techniques propose various ways of exposing and exploiting accuracy-efficiency tradeoffs. We present a taxonomy that classifies approximate computing techniques according to salient features: visibility, determinism, and coarseness. These axes allow us to address questions about the correctability, reproducibility, and control over accuracy-efficiency tradeoffs of different techniques. We use this taxonomy to inform research challenges in approximate architectures, compilers, and applications.