Title: Adaptive and Intelligent Battery-free Computing Systems: Platforms, Runtime Systems, and Tools

Abu Bakar

Ph.D. Candidate, Computer Science

School of Interactive Computing

Georgia Institute of Technology

https://abubakar.info/

Date: Tuesday, April 9, 2023

Time: 10:00 AM - 12:00 PM (EDT)

Location (in-person): CODA C1215

Location (remote): https://gatech.zoom.us/j/98426223669?pwd=bjZhcEFFbUxkOEtqRERPeDNKNVVKdz09&from=addon

Committee:

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Dr. Josiah Hester, School of Interactive Computing, Georgia Institute of Technology 

Dr. Ashutosh Dhekne, School of Computer Science, Georgia Institute of Technology 

Dr. Yingyan (Celine) Lin, School of Computer Science, Georgia Institute of Technology 

Dr. Przemysław Pawełczak, Embedded Systems Group, Delft University of Technology 

Dr. Alessandro Montanari, Pervasive Systems Group, Nokia Bell Labs 

Abstract:

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Energy-harvesting, battery-free devices enable deployment in new applications with their promise of zero maintenance and long lifetimes. A core challenge for these devices is maintaining usefulness despite varying energy availability, which causes power failures, loss of progress, and inconsistent execution. While prior research has enabled basic operability through power interruptions, performance optimization has often been overlooked, especially for emerging data-intensive Machine Learning (ML) applications that require on-device processing. 

This dissertation fills this gap by reimagining different components of the battery-free system stack. It introduces: i) runtime systems that equip batteryless applications with adaptability, ensuring they can operate effectively with higher throughputs in varying energy harvesting conditions, ii) reconfigurable and heterogeneous hardware platforms that enable the use of battery-free systems for modern compute-intensive inference-based applications, iii) novel ML algorithms for on-device energy-efficient inferences, and iv) user-facing tools and interfaces to streamline the development process of battery-free applications. 

In particular, this dissertation presents three systems created to imbue adaptability and intelligence into battery-free applications through energy-efficient software and novel hardware designs. Firstly, REHASH, a hardware-independent runtime system that uses lightweight signals and heuristics to capture changes in energy harvesting conditions and trigger application-level adaptation to get higher throughput in low-energy scenarios. Secondly, Protean, an energy-efficient and heterogeneous platform for adaptive and hardware-accelerated battery-free computing. Protean is an end-to-end system that leverages recent advancements in heterogeneous computing architecture to enable the development of modern, data-intensive, inference-based applications. Lastly, Lite-TM, a novel framework that enables the deployment of Tsetlin Machines (TM), a logic-based inference engine serving as an alternative to a deep neural network, on intermittently powered systems. Lite-TM employs advanced encoding schemes to compress TM models and incorporates three core techniques aimed at reducing the memory footprint of the trained TM models, accelerating model execution, and dynamically adjusting model complexity based on available energy.

These systems and their companion tools unlock many new applications and empower developers to quickly design, debug, and deploy sustainable, energy-efficient, adaptive, and intelligent battery-free applications.