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.

能源收获（EH）技术的成熟以及可行的间歇性计算的最新出现，该计算将收获的能量存储在能量存储中并支持程序执行的一集，创造了建立复杂无电计算系统的机会。该项目旨在在此类无电器设备中实现人工智能（AI）。但是，有两个主要挑战：1。大多数现有的深神经网络（DNN）很难适合资源受限的微控制器。 2。DNN通常需要多次执行发作才能获得一个推理结果，由于弱且不可预测的收获功率，可能需要无限期的时间。为了应对这些挑战，该项目正在开发多EXIT DNN，该DNN可以在每个执行情节中输出准确的推理结果。将执行三个任务，以奠定技术基础，以间歇性地推断EH功率的IoT设备。首先，将开发新颖的Power Trace意识压缩，在线修剪和适应算法，以确保在间歇性动力设备上有效部署多EXIT DNN。其次，将开发新的多EXIT统计和增量神经元网络（MESI-NN），以进一步降低潜伏期并提高准确性和能源效率。第三，将开发新的神经元体系结构搜索算法，以自动搜索最佳的MESI-NN体系结构。该项目将通过实际系统和应用程序进行评估，例如图像分类，关键字发现和活动识别。实现EH供电的无电池设备中的AI可以实现持续的事件驱动的灵敏度功能，其中主设备（例如，电池排水摄像机）可以保持不变，直到当它检测到感兴趣的事件时，直到被EH供电的设备唤醒。拟议的研究的社会影响是显着扩大部署在偏远地区的传感器和设备的寿命，这将极大地使各种消费者，商业，科学和国家安全应用受益。该项目将使学生了解相关的尖端知识和动手研究机会，并提高他们在面对当今竞争激烈的全球就业市场方面的能力和信心。拟议研究的教育影响包括基于两个PI的资源（例如DAC系统设计竞赛）的各种教育活动的整合；通过Pitt的Investing Now Summer School和ND的CS课程为印第安纳州的K-12学生提供宣传；本科研究重点是少数群体的参与以及研究成果的课程整合。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，认为通过评估被认为是珍贵的支持。

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