LAUSR

Autonomous mobile robots are receiving a lot of attention for many applications, such as package delivery and smart surveillance, however, the battery capacity is limited to implement intelligent decision making in robots because of the heavy computational costs. In this paper, an ultra-low-power artificial intelligence processor (AIP) is proposed for real-time decision making of autonomous mobile robots. To achieve low power consumption while maintaining high performance, it adopts four key features: 1) an 8-thread tree search processor for real-time path planning; 2) a reinforcement learning accelerator for the avoidance of unexpected obstacles; 3) a 3-level transposition table cache for the reduction of duplicated computations; and 4) a PVT compensation circuit for the stable operation at near-threshold voltage. The proposed 16 mm $^{{{2}}}$ AIP is fabricated using 65-nm triple-well CMOS technology. It consumes only 1.1 mW at 0.55 V supply voltage and 7 MHz operating frequency, and 151 mW at 1.2 V supply voltage and 245 MHz operating frequency. The AIP achieves fast search speed (470 000 search/s) and low energy consumption (79 nJ/search), and it is successfully applied to a battery-powered robot system for autonomous navigation without any collision in dynamic indoor environments.