MAFin: Maximizing Accuracy in FinFET based Approximated Real-Time Computing

We propose MAFin that exploits the unique temperature effect inversion (TEI) property of a FinFET based multicore platform, where processing speed increases with temperature, in the context of approximate real-time computing. In approximate real-time computing platforms, the execution of each task can be divided 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 (QoS) without violating deadlines. With an objective to maximize the QoS for a FinFET based multicore system, MAFin, our proposed real-time scheduler first derives a task-to-core allocation, while respecting system-wide constraints and prepares a schedule. During execution, MAFin further increases the achieved QoS, while balancing the performance and temperature on-the-fly by incorporating a prudential temperature cognizant frequency management mechanism and guarantees imposed constraints. Specifically, MAFin exploits the TEI property of FinFET based processors, where processor-speed is enhanced at the increased temperature, to reduce the execution time of the individual tasks. This reduced execution-time is then traded off either to enhance QoS by executing more from the tasks' optional parts or to improve energy efficiency by turning off the core. While surpassing prior art, MAFin achieves 70% QoS, which is further enhanced by 8.3% in online, with a maximum EDP gain of up to 12%, based on benchmark based evaluation on a 4-core based system.