Online Reinforcement Learning NoC for portable HD object recognition processor

Heterogeneous multi-core object recognition processor with Reinforcement Learning (RL) NoC is proposed for efficient portable HD object recognition. RL NoC automatically learns management policies in the network of heterogeneous system without an explicit modeling. By adopting RL NoC, 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 implemented chip achieves 121mW power consumption with 1.24 TOPS/W power efficiency.