SHF: Small: Disruptive Hardware for Energy-Starved Autonomous Nano-Systems

蜻蜓和虚拟昆虫等主要依靠能量清除的移动自主机器人昆虫，在执行动态环境导航中使用的底层软件算法时，由于数字处理器的过度功耗，经常会衰弱。这项NSF研究将研究潜在的自适应控制理论软件算法，以能够进行类脑强化学习的节能硬件取代它们。拟议的安装在机器人昆虫上的受生物学启发的硬件将有利于国防相关应用，对灾害地区进行远程监控的第一响应者，以及绘制敌对和危险环境的地图。这种算法硬件也将有利于飞行控制、生化过程、发电机和电信的应用。教育的改进和研究的整合是这一综合性和跨学科研究的主要目标，从而提供了机会来升级当前的计算机科学与工程课程，并培养未来的工程人才，他们将应用多学科的知识来设计超低功耗的自主纳米系统。自适应动态规划（ADP）算法应用于许多大型工程应用，涉及自适应最优控制系统和信号处理。然而，ADP在微处理器上的成功限制了其在移动计算和自主机器人昆虫领域的应用范围，在这些领域，电池节能是至关重要的。合理的ADP算法不能在便携式低功耗机器上运行，因为ADP需要大的内存库，并且需要并行处理以实现合理的运行时间。本研究旨在开发方法，将高级算法从微处理器上的软件实现首先移植到混合信号CMOS芯片设计中，然后通过结合CMOS和忆阻器技术设计超低能耗芯片。

Mobile autonomous robotic insects such as dragon flies and virtual bugs that chiefly rely on energy scavenging frequently languish due to the excessive power dissipation by digital processors while executing the underlying software algorithms used in navigation through dynamic environments. This NSF research will study the underlying adaptive control-theoretic software algorithms to replace them by energy-efficient hardware capable of brain-like reinforcement learning. The proposed biology-inspired hardware mounted on robotic insects will be beneficial to the defense related applications, first responders to disaster areas for remote monitoring, and mapping of hostile and hazardous environments. Such algorithmic hardware will also benefit applications in flight control, biochemical processes, power generators, and telecommunications. Educational improvement and research integration is a major goal of this integrative and cross-disciplinary research, thereby providing opportunity to upgrade the current Computer Science and Engineering curriculum and train engineering workforce of the future who will apply knowledge from multiple disciplines to design ultra-low-power autonomous nanosystems. Adaptive dynamic programming (ADP) algorithms are used in many large-scale engineering applications involving adaptive optimal control systems and signal processing. However, the success of ADP on the microprocessor has limited its scope in mobile computing and autonomous robotic insects where the battery energy preservation is paramount. Reasonable ADP algorithms cannot run on portable low power machines because ADP needs a large memory bank and requires parallel processing for a reasonable runtime. This research aims to develop methods for porting higher-level algorithms from software implementation on the microprocessor to a mixed signal CMOS chip design at first and then an ultra-low-energy chip design by combining CMOS and memristor technologies.