Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning lies at the intersection of embedded systems, robotics, and ML, compounding the challenges of these domains. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints; sensor, actuator, and compute hardware limitations; end-to-end system tradeoffs; and a large diversity of possible deployment scenarios. Tiny robot learning requires ML models to be designed with these challenges in mind, providing a crucible that reveals the necessity of holistic ML system design and automated end-to-end design tools for agile development. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.
翻译:机器学习(ML)已成为整个计算机系统的一个普遍工具。 正在出现的一种压力测试ML系统设计挑战的应用是小机器人学习、在资源限制的低成本自主机器人上部署ML、微小机器人学习位于嵌入系统、机器人和ML的交叉点,这加剧了这些领域的挑战。微小机器人学习受到来自大小、重量、面积和功率(SWAP)限制的挑战;传感器、动作器和计算硬件限制;端对端系统取舍;以及多种可能的部署情景。 小机器人学习需要ML模型在设计上与这些挑战相适应,提供显示整体ML系统设计和自动端对端设计工具对于灵活发展的必要性的熔炉。本文对小型机器人学习空间进行了简要调查,阐述了关键挑战,并为ML系统设计的未来工作提出了有希望的机会。