ARCTIC: Approximate Real-Time Computing in a Cache-Conscious Multicore Environment

Improving result-accuracy in approximate computing (AC)-based time-critical systems, without violating power constraints of the underlying circuitry, is gradually becoming challenging with the rapid progress in technology scaling. The execution span of each AC real-time tasks can be split into a couple of parts: 1) the mandatory part, execution of which offers a result of acceptable quality, followed by 2) the optional part, which can be executed partially or completely to refine the initially obtained result in order to increase the result-accuracy, while respecting the time constraint. In this article, we introduce a novel hybrid offline-online scheduling strategy, ARCTIC, for AC real-time tasks. The goal of real-time scheduler of ARCTIC is to maximize the results-accuracy (QoS) of the task set with opportunistic shedding of the optional part, while respecting system-wide constraints. During execution, ARCTIC retains exclusive copy of the private cache blocks only in the local caches in a multicore system and no copies of these blocks are maintained at the other caches, and improves performance (i.e., reduces execution-time) by accumulating more live blocks on-chip. Combining offline scheduling with the online cache optimization improves both QoS and energy efficiency. While surpassing prior arts, our proposed strategy reduces the task rejection rate by up to 25%, whereas enhances QoS by 10%, with an average energy-delay-product gain of up to 9.1%, on an 8-core system.