DELICIOUS: Deadline-Aware Approximate Computing in Cache-Conscious Multicore

Abstract

Enhancing result-accuracy in approximate computing (AC) based real-time systems, without violating power constraints of the underlying hardware, is a challenging problem. Execution of such AC real-time applications can be split into two parts: (i) the mandatory part, execution of which provides a result of acceptable quality, followed by (ii) the optional part, that can be executed partially or fully to refine the initially obtained result in order to increase the result-accuracy, without violating the time-constraint. This paper introduces DELICIOUS, a novel hybrid offline-online scheduling strategy for AC real-time dependent tasks. By employing an efficient heuristic algorithm, DELICIOUS first generates a schedule for a task-set with an objective to maximize the results-accuracy, while respecting system-wide constraints. During execution, DELICIOUS then introduces a prudential cache resizing that reduces temperature of the adjacent cores, by generating thermal buffers at the turned off cache ways. DELICIOUS further trades off this thermal benefits by enhancing the processing speed of the cores for a stipulated duration, called V/F Spiking, without violating the power budget of the core, to shorten the execution length of the tasks. This reduced runtime is exploited either to enhance result-accuracy by dynamically adjusting the optional part, or to reduce temperature by enabling sleep mode at the cores. While surpassing the prior art, DELICIOUS offers 80% result-accuracy with its scheduling strategy, which is further enhanced by 8.3% in online, while reducing runtime peak temperature by 5.8 °C on average, as shown by benchmark based evaluation on a 4-core based multicore.