Collaborative Research:CNS Core: Small: Intermittent and Incremental Inference with Statistical Neural Network for Energy-Harvesting Powered Devices

能量收集（EH）技术的成熟和最近出现的可行的间歇性计算（其将收集的能量存储在能量存储器中并支持程序执行的一个片段）创造了构建复杂的无电池计算系统的机会。该项目旨在在这种无电池设备中实现人工智能（AI）。然而，有两个主要的挑战：1。大多数现有的深度神经网络（DNN）难以适应资源受限的微控制器。2. DNN通常需要多个执行片段来获得一个推理结果，并且由于所获得的功率较弱且不可预测，因此可能需要不确定的时间。为了应对这些挑战，该项目正在开发多出口DNN，它可以在每个执行事件中输出增量准确的推理结果。将执行三项任务，为EH供电的物联网设备上的间歇性增量推理奠定技术基础。首先，将开发新颖的功率跟踪感知压缩、在线修剪和自适应算法，以确保多出口DNN在无源供电设备上的高效部署。其次，将开发新的多出口统计和增量神经网络（MESI-NN），以进一步减少延迟，提高准确性和能源效率。第三，将开发新的神经架构搜索算法，以自动搜索最佳MESI-NN架构。这个项目将与真实的系统和应用程序，如图像分类，关键字定位和活动识别进行评估。在EH供电的无电池设备中实现AI可以实现持久的事件驱动感测功能，其中主设备（例如电池耗尽的相机）可以保持关闭，直到EH供电的设备在检测到感兴趣的事件时唤醒。这项研究的社会影响是显著延长部署在偏远地区的传感器和设备的寿命，这将极大地有利于各种消费者，商业，科学和国家安全应用。该项目将使学生接触相关的前沿知识和实践研究机会，并提高他们面对当今竞争激烈的全球就业市场的能力和信心。拟议研究的教育影响包括根据两个PI可用的资源整合各种教育活动，例如DAC系统设计比赛;通过Pitt的Investing Now暑期学校和ND为印第安纳州的K-12学生提供的CS课程为当地K-12学生提供外展服务;本科生研究，重点是少数民族的参与，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.

The maturation of energy-harvesting (EH) technology and the recent emergence of viable intermittent computing, which stores harvested energy in an energy storage and supports an episode of program execution, creates the opportunity to build sophisticated batteryless computing systems. This project aims to realize artificial intelligence (AI) in such batteryless devices. However, there are two main challenges: 1. most existing Deep Neural Networks (DNNs) are hard to fit in resource-constrained microcontrollers. 2. DNNs usually require multiple execution episodes to obtain one inference result and it may take indefinite amount of time due to the weak and unpredictable harvested power. To address these challenges, this project is developing multi-exit DNNs, which can output incrementally accurate inference results during each execution episode. Three tasks will be carried out to lay the technological foundation for intermittent incremental inference on EH-powered IoT devices. First, novel power trace aware compression, online pruning and adaptation algorithms will be developed to ensure efficient deployment of multi-exit DNNs on intermittently-powered devices. Second, new multi-exit statistical and incremental neural networks (MESI-NN) will be developed to further reduce the latency and improve the accuracy and energy efficiency. Third, new neural architecture search algorithms will be developed to automatically search the best MESI-NN architecture. This project will be evaluated with real system and applications such as image classification, keyword spotting, and activity recognition. Realizing AI in EH-powered batteryless devices can enable persistent, event-driven sensing capabilities in which the main device (e.g. a battery-draining camera) can remain off until awaken by the EH-powered device when it detects events of interest. The societal impact of the proposed research is to significantly extend the lifetime of sensors and devices deployed in remote areas, which will drastically benefit various consumer, business, scientific and national security applications. This project will expose students to related cutting-edge knowledge and hands-on research opportunities and elevate their competence and confidence in facing of today's highly competitive global job market. The education impact of the proposed research includes the integration of various education activities based on the resources available to the two PIs such as DAC System Design Contest; outreach for local K-12 students through Pitt’s Investing Now summer school and ND’s CS curriculum for K-12 students in Indiana; undergraduate research with emphasis on minority participation, and course integration of the research outcomes.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.