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

Abstract

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.