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

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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.