MyML: User-Driven Machine Learning

Machine learning (ML) on resource-constrained edge devices is expensive and often requires offloading computation to the cloud, which may compromise the privacy of user data. In contrast, the type of data processed at edge devices is user specific and limited to few inference classes. In this work,...

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Bibliographic Details
Published in:2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 145 - 150
Main Authors: Goyal, Vidushi, Bertacco, Valeria, Das, Reetuparna
Format: Conference Proceeding
Language:English
Published: IEEE 05.12.2021
Subjects:
Buildings
Collaboration
Computational modeling
Data privacy
Design automation
Privacy
Transfer learning
Online Access:Get full text
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Summary:Machine learning (ML) on resource-constrained edge devices is expensive and often requires offloading computation to the cloud, which may compromise the privacy of user data. In contrast, the type of data processed at edge devices is user specific and limited to few inference classes. In this work, we explore the opportunity of building smaller, user-specific machine learning models, rather than utilizing a generic, compute-intensive machine learning model that caters to a diverse range of users. We first present a hardware-friendly, light-weight pruning technique to create user-specific models directly on mobile platforms, while simultaneously executing inferences. The proposed technique leverages compute sharing between pruning and inference, customizes the retraining backward-pass and chooses a pruning granularity for efficient processing on edge. We then propose architectural support to prune user-specific models on a systolic edge ML inference accelerator. We demonstrate that user-specific models provide a speedup of 2.3\times over the generic model on mobile CPUs.
DOI:10.1109/DAC18074.2021.9586320