Dynamic effort scaling: managing the quality-efficiency tradeoff

Several recently proposed design techniques leverage the inherent error resilience of applications for improved efficiency (energy or performance). Hardware and software systems that are thus designed may be viewed as "scalable effort systems", since they offer the capability to modulate the effort that they expend towards computation, thereby allowing for tradeoffs between output quality and efficiency.

We propose the concept of Dynamic Effort Scaling (DES), which refers to dynamic management of the control knobs that are exposed by scalable effort systems. We argue the need for DES by observing that the degree of resilience often varies significantly across applications, across datasets, and even within a dataset. We propose a general conceptual framework for DES by formulating it as a feedback control problem, wherein the scaling mechanisms are regulated with the goal of maintaining output quality within a certain specified limit. We present an implementation of Dynamic Effort Scaling in the context of a scalable-effort processor for Support Vector Machines, and evaluate it under various application scenarios and data sets. Our results clearly demonstrate the benefits of the proposed approach --- statically setting the scaling mechanisms leads to either significant error overshoot or significant opportunities for energy savings left on the table unexploited. In contrast, DES is able to effectively regulate the output quality while maximally exploiting the time-varying resiliency in the workload.