Energy-efficient mapping of biomedical applications on domain-specific accelerator under process variation

The variability of deep-submicron technologies creates systems with asymmetric cores from a frequency and leakage power viewpoint, which makes an opportunity for performance-power optimization. In particular, process variation can transform a homogeneous many-core platform into a heterogeneous system where the task mapping becomes extremely difficult. In this paper, we propose a mapping algorithm that selects an appropriate task mapping along with voltage and frequency assignment for a cluster of cores. The mapping algorithm, which is based on simulated annealing, determines cluster voltages and core frequencies to minimize energy consumption and EDP under process variation. We examine the effectiveness of our proposed algorithm on a fully placed and routed 128-core biomedical accelerator in 45nm when running various applications including compressive sensing, seizure detection and ultrasound spectral Doppler and linear regression. The results indicate that exposing frequency and power variations to the mapping algorithm results in up to 22% (on average 11%) energy saving and 31% (on average 19%) EDP improvement.