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（将收集的能量存储在能量存储中并支持程序执行的一个片段）为构建复杂的无电池计算系统创造了机会。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）很难适应资源有限的微控制器。2. dnn通常需要多次执行才能获得一个推理结果，并且由于收获的功率弱且不可预测，可能需要无限长的时间。为了应对这些挑战，该项目正在开发多出口深度神经网络，它可以在每个执行集输出增量准确的推理结果。将开展三项工作，为eh驱动物联网设备的间歇增量推理奠定技术基础。首先，将开发新的功率跟踪感知压缩、在线修剪和自适应算法，以确保在间歇性供电设备上有效部署多出口dnn。其次，将开发新的多出口统计和增量神经网络（MESI-NN），进一步降低延迟，提高精度和能源效率。第三，开发新的神经网络结构搜索算法，自动搜索最佳MESI-NN结构。本项目将以真实系统和应用进行评估，如图像分类、关键词识别和活动识别。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传感功能，其中主设备（例如耗电相机）可以保持关闭状态，直到eh供电的设备在检测到感兴趣的事件时唤醒它。拟议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使用寿命，这将大大有利于各种消费者、商业、科学和国家安全应用。该项目将使学生接触到相关的前沿知识和实践研究机会，提高他们的能力和信心，以面对当今竞争激烈的全球就业市场。建议的研究对教育的影响包括基于两个pi可用资源的各种教育活动的整合，如DAC系统设计竞赛；通过皮特的“现在投资”暑期学校和ND为印第安纳州K-12学生开设的CS课程，为当地K-12学生提供服务；强调少数民族参与的本科生研究，以及研究成果的课程整合。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.