Rumba

Abstract

Approximate computing can be employed for an emerging class of applications from various domains such as multimedia, machine learning and computer vision. The approximated output of such applications, even though not 100% numerically correct, is often either useful or the difference is unnoticeable to the end user. This opens up a new design dimension to trade off application performance and energy consumption with output correctness. However, a largely unaddressed challenge is quality control: how to ensure the user experience meets a prescribed level of quality. Current approaches either do not monitor output quality or use sampling approaches to check a small subset of the output assuming that it is representative. While these approaches have been shown to produce average errors that are acceptable, they often miss large errors without any means to take corrective actions. To overcome this challenge, we propose Rumba for online detection and correction of large approximation errors in an approximate accelerator-based computing environment. Rumba employs continuous lightweight checks in the accelerator to detect large approximation errors and then fixes these errors by exact re-computation on the host processor. Rumba employs computationally inexpensive output error prediction models for efficient detection. Computing patterns amenable for approximation (e.g., map and stencil) are usually data parallel in nature and Rumba exploits this property for selective correction. Overall, Rumba is able to achieve 2.1x reduction in output error for an unchecked approximation accelerator while maintaining the accelerator performance gains at the cost of reducing the energy savings from 3.2x to 2.2x for a set of applications from different approximate computing domains.