Quality Optimization of Resilient Applications under Temperature Constraints.

Inherent resilience of applications enables the design paradigm of approximate computing that exploits computation in-exactness by trading off output quality for runtime system resources. When executing such quality-scalable applications on multiprocessor embedded systems, it is expected not only to achieve the highest possible output quality, but also to handle the critical thermal challenge spurred by vastly increased chip density. While the rising temperature causes significant quality distortion at runtime, existing thermal-management techniques, such as dynamic frequency scaling, rarely take into account the trade-off possibilities between output quality and thermal budget. In this paper, we explore the application-level quality-scaling features of resilient applications to achieve effective temperature control as well as quality maximization. We propose an efficient iterative pseudo quadratic programming heuristic to decide the optimal frequency and application execution cycles, in order to achieve quality optimization, under temperature, timing, and energy constraints. Our approaches are evaluated using realistic benchmarks with known platform thermal parameters. The proposed methods show a 98.5% quality improvement with temperature violation awareness.