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

SHF：小型：能源匮乏的自主纳米系统的颠覆性硬件

• 批准号: 1421467
• 负责人: Mikhail Erementchouk
• 金额: $ 40万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421467&HistoricalAwards=false
• 关键词: SHF; Small; Disruptive; Hardware; Energy

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.

移动的自主机器昆虫，如蜻蜓和虚拟昆虫，主要依赖于能量收集，经常由于数字处理器的过度功耗，同时执行在动态环境中导航所使用的底层软件算法而枯萎。NSF的这项研究将研究底层的自适应控制理论软件算法，以便用能够进行类脑强化学习的节能硬件来取代它们。安装在机器人昆虫上的拟议生物启发硬件将有利于国防相关应用，灾区的远程监控以及恶劣和危险环境的测绘。此类算法硬件还将有利于飞行控制、生化过程、发电机和电信领域的应用。教育改进和研究整合是这种综合性和跨学科研究的主要目标，从而提供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员，他们将应用多个学科的知识来设计超低功耗自主纳米系统。自适应动态规划（ADP）算法用于许多涉及自适应最优控制系统和信号处理的大规模工程应用中。然而，ADP在微处理器上的成功限制了其在移动的计算和自主机器昆虫中的范围，其中电池能量保存是至关重要的。合理的ADP算法不能在便携式低功耗机器上运行，因为ADP需要大的存储器组，并且需要并行处理以获得合理的运行时间。本研究的目的是开发方法移植更高层次的算法从软件实现的微处理器上的混合信号CMOS芯片的设计，首先，然后通过结合CMOS和忆阻器技术的超低功耗芯片设计。