DVAS: Dynamic Voltage Accuracy Scaling for increased energy-efficiency in approximate computing

Abstract

A wide variety of existing and emerging applications in recognition, mining and synthesis and machine-to-human interactions tolerate small errors or deviations in their computational results. Digital systems can exploit this error tolerance to increase their energy efficiency, which is crucial in high performance wearable electronics and in emerging low power systems for the internet-of-things. A dynamic energy-accuracy trade-off brings an extra degree of freedom for system level power management. We introduce the concept of Dynamic Voltage Accuracy Scaling and illustrate its analogy to Dynamic Voltage Frequency Scaling. Dynamic Voltage Accuracy Scaling proves to have higher energy gains at most output qualities compared to other approximate computing alternatives. This work further generalizes the Dynamic Voltage Accuracy Scaling concept to pipelined structures and quantifies its energy overhead. Shallow pipelined multipliers with two to four dynamic accuracy modes can be supported with limited (<; 10-20%) overhead, resulting in significant energy savings of up to 90% or more for less than 2% mean error. DVAS is finally applied to a JPEG image processing application, demonstrating large system level gains without noticeable impact to user or application.