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

Chakraborty, Shounak; Saha, Sangeet; Sjalander, Magnus; Mcdonald-Maier, Klaus

Abstract

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.

Citation

Chakraborty, S., Saha, S., Sjalander, M., & Mcdonald-Maier, K. (2024, June). MAFin: Maximizing Accuracy in FinFET based Approximated Real-Time Computing. Presented at DAC '24: 61st ACM/IEEE Design Automation Conference, San Francisco CA USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: DAC '24: 61st ACM/IEEE Design Automation Conference
• Start Date: Jun 23, 2024
• End Date: Jun 27, 2024
• Acceptance Date: Dec 15, 2023
• Online Publication Date: Nov 7, 2024
• Publication Date: Nov 7, 2024
• Deposit Date: Jan 9, 2025
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Article Number: 92
• Pages: 1-6
• Book Title: DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference
• ISBN: 9798400706011
• DOI: https://doi.org/10.1145/3649329.3655985
• Public URL: https://durham-repository.worktribe.com/output/3329035