Approximate computing: Energy-efficient computing with good-enough results

Abstract

Summary form only given. With the explosion in digital data, computing platforms are increasingly being used to execute applications (such as web search, data analytics, sensor data processing, recognition, mining, and synthesis) for which “correctness” is defined as producing results that are good enough, or of sufficient quality. Such applications invariably demonstrate a high degree of inherent resilience to their underlying computations being executed in an approximate manner. This inherent resilience is due to several factors including redundancy in the input data, the statistical nature of the computations themselves, and the acceptability (often, inevitability) of less-than-perfect results. Approximate computing is an approach to designing systems that are more efficient, by leveraging the inherent resilience of applications. We will outline a range of approximate computing techniques that we have developed from software to architecture to circuits, which have shown promising results. We conclude with a discussion of some of the challenges that need to be addressed to facilitate a broader adoption of approximate computing.