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Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems

Authors:

Graham Gobieski,

Nathan BeckmannAuthors Info & Claims

ASPLOS '19: Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems

Pages 199 - 213

https://doi.org/10.1145/3297858.3304011

Published: 04 April 2019 Publication History

Abstract

Energy-harvesting technology provides a promising platform for future IoT applications. However, since communication is very expensive in these devices, applications will require inference "beyond the edge" to avoid wasting precious energy on pointless communication. We show that application performance is highly sensitive to inference accuracy. Unfortunately, accurate inference requires large amounts of computation and memory, and energy-harvesting systems are severely resource-constrained. Moreover, energy-harvesting systems operate intermittently, suffering frequent power failures that corrupt results and impede forward progress. This paper overcomes these challenges to present the first full-scale demonstration of DNN inference on an energy-harvesting system. We design and implement SONIC, an intermittence-aware software system with specialized support for DNN inference. SONIC introduces loop continuation, a new technique that dramatically reduces the cost of guaranteeing correct intermittent execution for loop-heavy code like DNN inference. To build a complete system, we further present GENESIS, a tool that automatically compresses networks to optimally balance inference accuracy and energy, and TAILS, which exploits SIMD hardware available in some microcontrollers to improve energy efficiency. Both SONIC & TAILS guarantee correct intermittent execution without any hand-tuning or performance loss across different power systems. Across three neural networks on a commercially available microcontroller, SONIC & TAILS reduce inference energy by 6.9× and 12.2×, respectively, over the state-of-the-art.

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Cited By

Rahman TMao GMaheshwari SShafik RYakovlev A(2024)MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546779(1-6)Online publication date: 25-Mar-2024
https://doi.org/10.23919/DATE58400.2024.10546779
Song WKaxiras SVoigt TYao YMottola LShu YLiu JTan RHe YChen J(2024)TaDA: Task Decoupling Architecture for the Battery-less Internet of ThingsProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699347(409-421)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699347
Custode LFarina PYildiz EKilic RYildirim KIacca GShu YLiu JTan RHe YChen J(2024)Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless EdgeProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699335(239-252)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699335
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Index Terms

Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded software

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ASPLOS '19: Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems

April 2019

1126 pages

ISBN:9781450362405

DOI:10.1145/3297858

General Chairs:
Iris Bahar
Brown University
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Maurice Herlihy
Brown University
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Emmett Witchel
University of Texas, Austin
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Alvin Lebeck
Duke University

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Cited By

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https://doi.org/10.23919/DATE58400.2024.10546779
Song WKaxiras SVoigt TYao YMottola LShu YLiu JTan RHe YChen J(2024)TaDA: Task Decoupling Architecture for the Battery-less Internet of ThingsProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699347(409-421)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699347
Custode LFarina PYildiz EKilic RYildirim KIacca GShu YLiu JTan RHe YChen J(2024)Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless EdgeProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699335(239-252)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699335
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Alsubhi ABabatunde STobias NSorber J(2024)Stash: Flexible Energy Storage for Intermittent SensorsACM Transactions on Embedded Computing Systems10.1145/364151123:2(1-23)Online publication date: 19-Jan-2024
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