Issue No. 05 - September/October (2017 vol. 37)
Youchang Kim , Korea Advanced Institute of Science and Technology
Dongjoo Shin , Korea Advanced Institute of Science and Technology
Jinsu Lee , Korea Advanced Institute of Science and Technology
Hoi-Jun Yoo , Korea Advanced Institute of Science and Technology
Autonomous robots are actively studied for many unmanned applications, however, the heavy computational costs and limited battery capacity make it difficult to implement intelligent decision making in robots. In this article, the authors propose a low-power deep search engine (code-named “BRAIN”) for real-time path planning of intelligent autonomous robots. To achieve low power consumption while maintaining high performance, BRAIN adopts a multithreaded core architecture with a transposition table cache to detect and avoid duplicated searches between the processors at the deeper level of the search tree. In addition, dynamic voltage and frequency scaling is adopted to minimize power consumption without any loss of performance because the workload is gradually decreasing while approaching the target position. BRAIN achieves fast search speed (470,000 searches per second) and low energy consumption (79 nJ per search), and it is successfully applied to the robots for autonomous navigation without any collision in dynamic environments.
Program processors, Mobile robots, Path planning, System-on-chip, Computer architecture, Search engines,multiple datastream architectures, multiprocessors, special-purpose systems, application-based systems, problem solving, control methods, search, robotics
Youchang Kim, Dongjoo Shin, Jinsu Lee, Hoi-Jun Yoo, "BRAIN: A Low-Power Deep Search Engine for Autonomous Robots", IEEE Micro, vol. 37, no. , pp. 11-19, September/October 2017, doi:10.1109/MM.2017.3711641