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

合作研究：CNS 核心：小型：利用统计神经网络对能量收集供电设备进行间歇和增量推理

• 批准号: 2007274
• 负责人: Jingtong Hu
• 金额: $ 25万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007274&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

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.

能量收集（EH）技术的成熟以及最近出现的可行的间歇计算（将收集的能量存储在能量存储器中并支持一段程序执行）创造了构建复杂的无电池计算系统的机会。该项目旨在实现这一目标。然而，这种无电池设备中的人工智能 (AI) 存在两个主要挑战： 1. 大多数现有深度神经网络 (DNN) 很难适应资源有限的微控制器 2. DNN 通常需要多次执行。为了解决这些挑战，该项目正在开发多出口 DNN，它可以在每次执行期间输出增量准确的推理结果。首先，将开发新型功率跟踪感知压缩、在线修剪和自适应，以确保多出口 DNN 在算法上的高效部署。其次，将开发新的多出口统计和增量神经网络（MESI-NN），以进一步减少延迟并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来自动搜索。该项目将通过图像分类、关键词识别和活动识别等真实系统和应用进行评估，在 EH 供电的无电池设备中实现人工智能可以实现持久的、事件驱动的传感功能，其中主要功能是。设备（例如该研究的社会影响是显着延长部署在偏远地区的传感器和设备的使用寿命，这将极大地造福于各种情况。该项目将为学生提供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。包括根据现有资源整合各种教育活动两个 PI，例如 DAC 系统设计竞赛；通过 Pitt 的 Investing Now 暑期学校和 ND 面向印第安纳州 K-12 学生的计算机科学课程进行推广，重点是少数族裔参与以及研究的课程整合；该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices

• DOI: 10.1109/dac18072.2020.9218526
• 发表时间: 2020-04
• 期刊: 2020 57th ACM/IEEE Design Automation Conference (DAC)
• 作者: Yawen Wu;Zhepeng Wang;Zhenge Jia;Yiyu Shi;J. Hu

Lightweight Run-Time Working Memory Compression for Deployment of Deep Neural Networks on Resource-Constrained MCUs

• DOI: 10.1145/3394885.3439194
• 发表时间: 2021-01
• 期刊: 2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC)
• 作者: Zhepeng Wang;Yawen Wu;Zhenge Jia;Yiyu Shi;J. Hu

Implementation of Multi-Exit Neural-Network Inferences for an Image-Based Sensing System with Energy Harvesting

• DOI: 10.3390/jlpea11030034
• 发表时间: 2021-09
• 期刊: Journal of Low Power Electronics and Applications
• 影响因子: 2.1
• 作者: Yuyang Li;Yuxin Gao;Minghe Shao;Joseph T. Tonecha;Yawen Wu;Jingtong Hu;Inhee Lee

Developing a Miniature Energy-Harvesting-Powered Edge Device with Multi-Exit Neural Network

• DOI: 10.1109/iscas51556.2021.9401799
• 发表时间: 2021-05
• 期刊: 2021 IEEE International Symposium on Circuits and Systems (ISCAS)
• 作者: Yuyang Li;Yawen Wu;Xincheng Zhang;Ehab A. Hamed;Jingtong Hu;Inhee Lee

Energy-Aware Adaptive Multi-Exit Neural Network Inference Implementation for a Millimeter-Scale Sensing System

毫米级传感系统的能量感知自适应多出口神经网络推理实现

• DOI: 10.1109/tvlsi.2022.3171308
• 发表时间: 2022
• 期刊: IEEE Transactions on Very Large Scale Integration (VLSI
• 作者: Li, Yuyang;Wu, Yawen;Zhang, Xincheng;Hu, Jingtong;Lee, Inhee
• 通讯作者: Lee, Inhee