research-article

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LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms

Authors:

Jagmohan Chauhan,

Stylianos I. Venieris,

Cecilia MascoloAuthors Info & Claims

SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

Pages 138 - 151

https://doi.org/10.1145/3625687.3625804

Published: 26 April 2024 Publication History

Abstract

Continual Learning (CL) allows applications such as user personalization and household robots to learn on the fly and adapt to context. This is an important feature when context, actions, and users change. However, enabling CL on resource-constrained embedded systems is challenging due to the limited labeled data, memory, and computing capacity.

In this paper, we propose LifeLearner, a hardware-aware meta continual learning system that drastically optimizes system resources (lower memory, latency, energy consumption) while ensuring high accuracy. Specifically, we (1) exploit meta-learning and rehearsal strategies to explicitly cope with data scarcity issues and ensure high accuracy, (2) effectively combine lossless and lossy compression to significantly reduce the resource requirements of CL and rehearsal samples, and (3) developed hardware-aware system on embedded and IoT platforms considering the hardware characteristics.

As a result, LifeLearner achieves near-optimal CL performance, falling short by only 2.8% on accuracy compared to an Oracle baseline. With respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically reduces the memory footprint (by 178.7×), end-to-end latency by 80.8--94.2%, and energy consumption by 80.9--94.2%. In addition, we successfully deployed LifeLearner on two edge devices and a microcontroller unit, thereby enabling efficient CL on resource-constrained platforms where it would be impractical to run SOTA methods and the far-reaching deployment of adaptable CL in a ubiquitous manner. Code is available at https://github.com/theyoungkwon/LifeLearner.

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Cited By

Zhu SVoigt TRahimian FKo J(2024)On-device Training: A First Overview on Existing SystemsACM Transactions on Sensor Networks10.1145/369600320:6(1-39)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1145/3696003
Yu XThomas AMoreno IGutierrez LRosing T(2024)Intelligence Beyond the Edge using Hyperdimensional Computing2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00005(1-13)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00005

Index Terms

LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Human-centered computing
  1. Ubiquitous and mobile computing

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Published In

cover image ACM Conferences

SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

November 2023

574 pages

ISBN:9798400704147

DOI:10.1145/3625687

General Chair:
Rasit Eskicioglu,
Program Chair:
Polly Huang,
Program Co-chair:
Neal Patwari

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 26 April 2024

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SenSys '23

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SenSys '23: 21st ACM Conference on Embedded Networked Sensor Systems

November 12 - 17, 2023

Istanbul, Turkiye

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Overall Acceptance Rate 174 of 867 submissions, 20%

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SenSys '24

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The 22nd ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2024

Hangzhou , China

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Zhu SVoigt TRahimian FKo J(2024)On-device Training: A First Overview on Existing SystemsACM Transactions on Sensor Networks10.1145/369600320:6(1-39)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1145/3696003
Yu XThomas AMoreno IGutierrez LRosing T(2024)Intelligence Beyond the Edge using Hyperdimensional Computing2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00005(1-13)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00005

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