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极低成本和高能效的智能电子设备的巨大需求。由于技术规模的进一步扩大，传统的数字技术面临着计算瓶颈。因此，迫切需要新的计算方法来满足机器学习赋能应用的需求。该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效。该项目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，潜在地为物联网/人工智能的发展带来重大进展。该项目还将通过讲习班和培训计划相结合的方式，向K-12学生和代表性不足的群体传播关于节能边缘处理的研究，从而为社会创造重要的外展活动。将向本科生和研究生介绍新的课程材料和动手实验，以促进计算机工程从算法到硬件设计的跨层学习策略。这个项目的目的是为利用时间进行信息处理的新兴的时域计算开发一种系统的设计方法。拟议的发展涵盖了一系列技术，包括电路设计、电子设计自动化、机器学习算法的实施以及近传感器计算的集成。具体地说，该项目将致力于：(1)制定全面的设计原则，(2)开发与现代商业工具兼容并能够大规模实施该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方面的强大优势，以及(4)与智能边缘处理设备的传感器节点进行系统集成。该项目将提供一种高度自动化的系统设计方法，使新的时间域计算技术能够大规模集成并提高能效。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.