An energy-aware self-adaptive System-on-Chip architecture for real-time Harris corner detection with multi-resolution support

摘要

The proliferation of embedded vision in today's life has necessitated the development of System-on Chips to perform utmost processing in a single chip rather than discrete components. Embedded vision is bounded by stringent requirements, namely real-time performance, limited energy, and adaptivity to cope with the standards evolution. In this article, an energy-aware self-adaptive System-on-Chip for real-time corner detection is realized on Zynq All Programmable System-on-Chip using Dynamic Partial Reconfiguration. A careful analysis of algorithm and efficient utilization of Zynq resources results in highly parallelized and pipelined architecture outperforms the state-of-the-art. A context-aware configuration scheduler application is developed to adhere to operating context and trades off between video resolution and energy consumption to sustain the uttermost operation time for battery-powered devices while delivering real-time performance. The experiments show that the self-adaptive method achieves 1.77 times longer operation time than a parametrized IP core, with negligible reconfiguration energy overhead. A marginal effect of partial reconfiguration overhead on performance is observed, for instance, only two video frames are dropped for HD1080p60 during the reconfiguration time. (C) 2016 Elsevier B.V. All rights reserved.