CNN Workloads Characterization and Integrated CPU-GPU DVFS Governors on Embedded Systems

Dynamic power management (DPM) techniques on mobile systems are indispensable for deep learning (DL) inference optimization, which is mainly performed on battery-based mobile and/or embedded platforms with constrained resources. To this end, we characterize CNN workloads using object detection applications of YOLOv4/-tiny and YOLOv3/-tiny, and then propose integrated CPU-GPU DVFS governor policies that scale integrated pairs of CPU and GPU frequencies to improve energy-delay product (EDP) with negligible inference execution time degradation. Our results show up to 16.7% EDP improvements with negligible (mostly less than 2%) performance degradation using object detection applications on NVIDIA Jetson TX2.