BRAIN: A Low-Power Deep Search Engine for Autonomous Robots
BRAIN: A Low-Power Deep Search Engine for Autonomous Robots
复制标题
BRAIN：用于自主机器人的低功耗深度搜索引擎
作者：
Youchang Kim;Dongjoo Shin;Jinsu Lee;H. Yoo
自主机器人在许多无人应用中得到了积极的研究，然而，繁重的计算成本和有限的电池容量使得机器人很难实现智能决策。在本文中，作者提出了一种低功耗深度搜索引擎（代号“BRAIN”），用于智能自主机器人的实时路径规划。为了在保持高性能的同时实现低功耗，BRAIN 采用了带有转置表缓存的多线程核心架构，可以在搜索树的更深层次上检测并避免处理器之间的重复搜索。此外，由于工作负载在接近目标位置时逐渐减少，因此采用动态电压和频率缩放来最大限度地降低功耗，而不会损失任何性能。 BRAIN实现了快速搜索（每秒470,000次搜索）和低能耗（每次搜索79 nJ），并成功应用于动态环境中无碰撞自主导航的机器人。
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.