Intelligent Network-on-Chip With Online Reinforcement Learning for Portable HD Object Recognition Processor

Abstract

An intelligent Reinforcement Learning (RL) Network-on-Chip (NoC) is proposed as a communication architecture of a heterogeneous many-core processor for portable HD object recognition. The proposed RL NoC automatically learns bandwidth adjustment and resource allocation in the heterogeneous many-core processor without explicit modeling. By regulating the bandwidth and reallocating cores, the throughput performances of feature detection and description are increased by 20.4% and 11.5%, respectively. As a result, the overall execution time of the object recognition is reduced by 38%. The proposed processor with RL NoC is implemented in a 65 nm CMOS process, and it successfully demonstrates the real-time object recognition for a 720 p HD video stream while consuming 235 mW peak power at 200 MHz, 1.2 V.