DELICIOUS: Deadline-Aware Approximate Computing in Cache-Conscious Multicore
Sangeet Saha, Shounak Chakraborty, Sukarn Agarwal, Rahul Gangopadhyay, Magnus Själander, Klaus Dieter McDonald-Maier · IEEE Transactions on Parallel and Distributed Systems · 2022
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 article introducesDELICIOUS, a novel hybrid offline-onlinescheduling strategyfor AC real-time dependent tasks. By employing an efficientheuristic algorithm,DELICIOUSfirst generates a schedule for a task-set with an objective to maximize the results-accuracy, while respecting system-wide constraints. During execution,DELICIOUSthen introduces aprudential cache resizingthat reduces temperature of the adjacent cores, by generating thermal buffers at the turned off cache ways.DELICIOUSfurther trades off this thermal benefits by enhancing the processing speed of the cores for a stipulated duration, calledV/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,DELICIOUSoffers 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.