Energy efficient mapping on manycore with dynamic and partial reconfiguration: Application to a smart camera

• Published in: International journal of circuit theory and applications 2018-09, Vol.46 (9), p.1648-1662
• Main Authors: Bonamy, Robin, Bilavarn, Sébastien, Muller, Fabrice, Duhem, François, Heywood, Simon, Millet, Philippe, Lemonnier, Fabrice
• Format: Article
• Language: English
• Subjects: Acceleration; Computer Science; dynamic partial reconfiguration; Embedded Systems; Energy efficiency; Gate arrays; Hardware; Image processing; manycore; Mapping; multicore; Multiprocessing; Power efficiency; Reconfiguration; Scheduling; smart camera
• ISSN: 0098-9886; 1097-007X
• DOI: 10.1002/cta.2508

Summary This paper describes a methodology to improve the energy efficiency of high‐performance multiprocessor architectures with dynamic and partial reconfiguration (DPR), based on a thorough application study in the field of smart camera technology. Field‐programmable gate arrays are increasingly being used in cameras owing to their suitability for real‐time image processing with intensive, high‐performance tasks and to the recent advances in dynamic reconfiguration that further improve energy efficiency. The approach used to best exploit DPR is based on the better coupling of 2 decisive elements in the problem of heterogeneous deployment: design space exploration and advanced scheduling. We show how a tight integration of exploration, energy‐aware scheduling, common power models, and decision support in heterogeneous DPR multiprocessor system‐on‐a‐chip mapping can be used to improve the energy efficiency of hardware acceleration. Applying this to a mobile vehicle license‐plate tracking and recognition service results in up to a 19‐fold improvement in energy efficiency compared with software multiprocessor execution (in terms of energy‐delay product) and up to more than a threefold improvement compared with a multiprocessor with static hardware acceleration (ie, without DPR). This paper describes a methodology to improve the energy efficiency of high‐performance multiprocessor architectures with dynamic and partial reconfiguration based on a thorough application study in the field of smart camera technology. Results on a mobile vehicle license‐plate recognition service show up to a 19‐fold improvement in energy efficiency compared with software multiprocessor execution (in terms of energy‐delay product) and up to more than a threefold improvement compared with a multiprocessor with static hardware acceleration (ie, without dynamic and partial reconfiguration).