CAREER: Design and Synthesis of Energy-efficient Time-domain Computing for Intelligent Edge Processing

物联网（IoT）和人工智能（AI）的快速增长正在创造对低成本和高能效智能电子设备的巨大需求。 传统的数字技术由于难以进一步扩展而面临计算瓶颈。 因此，迫切需要新的计算方法来满足机器学习授权应用的需求。 这个项目探索了一种非传统的时域计算技术，有望从根本上提高计算的能源效率。 该项目可以通过在小型物联网设备或传感器节点中实现昂贵的机器学习操作，为物联网/人工智能的发展带来重大进步。该项目还将通过研讨会和培训计划的结合，向K-12学生和代表性不足的群体传播节能边缘处理的研究，从而为社会创造重要的推广活动。 新的课程材料和动手实验将介绍给本科生和研究生，以促进从算法到计算机工程硬件设计的跨层学习策略。 本计画旨在发展一套系统化的设计方法，以应用于利用时间进行资讯处理的新兴时域计算。 拟议的开发涵盖了一系列技术，包括电路设计、电子设计自动化、机器学习算法的实现以及近传感器计算的集成。 具体而言，该项目将致力于：（1）开发时域计算的彻底设计原则，（2）开发与现代商业工具兼容并能够大规模实现技术的设计自动化方法，（3）探索其在机器学习和其他流行计算算法中的强大优势，以及（4）与用于智能边缘处理设备的传感器节点进行系统集成。该项目将提供一个高度自动化的系统化设计方法，使大规模集成和提高新的时域计算技术的能源效率。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The rapid growth in Internet-of-Things (IoT) and Artificial Intelligence (AI) is creating a huge demand for intelligent electronic devices with very low cost and high energy efficiency. Conventional digital technology is faced with computing bottleneck due to the difficulty of further technology scaling. As a result, new computing methods are urgently needed to meet the demand from the machine learning empowered applications. This project explores a non-conventional time-domain computing technique with promise of bringing fundamental improvement to the energy efficiency of computing. The project can potentially bring significant advancement to the IoT/AI developments by enabling expensive machine learning operations in small IoT devices or sensor nodes. This project will also create significant outreach activities to the society by disseminating the study of energy efficient edge processing to K-12 students and underrepresented groups through a combination of workshops and training programs. New course materials and hand-on experiments will be introduced to undergraduate and graduate students to promote a cross-layer learning strategy from algorithm to hardware design for computer engineering. This project aims at developing a systematic design approach for the emerging time-domain computing which utilizes time for information processing. The proposed developments cover a range of techniques including circuit design, electronic design automation, implementation of machine learning algorithms and integration for near-sensor computing. Specifically, the project will be dedicated to: (1) developing a thorough design principles of the time-domain computing, (2) developing a design automation methodology that is compatible with modern commercial tools and capable of large-scale implementation of the techniques, (3) exploring its strong benefits in machine learning and other popular computing algorithms, and (4) performing system integration with sensor nodes for intelligent edge processing devices. The project will deliver a highly automated systematic design methodology enabling large-scale integration and enhanced energy-efficiency for new time-domain computing techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.