P2W: From Power Traces to Weights Matrix -- An Unconventional Transfer Learning Approach

TL;DR

This paper introduces a novel transfer learning method that extracts weights from an embedded system's power traces to initialize new ML models, significantly improving training efficiency and accuracy without direct model access.

Contribution

It proposes an unconventional transfer learning technique that uses power consumption measurements to approximate model weights, enabling training without direct access to the original model.

Findings

Increases model accuracy up to 3 times with limited data

Effective for embedded systems without model access

Improves training efficiency in data-scarce scenarios

Abstract

The rapid growth of deploying machine learning (ML) models within embedded systems on a chip (SoCs) has led to transformative shifts in fields like healthcare and autonomous vehicles. One of the primary challenges for training such embedded ML models is the lack of publicly available high-quality training data. Transfer learning approaches address this challenge by utilizing the knowledge encapsulated in an existing ML model as a starting point for training a new ML model. However, existing transfer learning approaches require direct access to the existing model which is not always feasible, especially for ML models deployed on embedded SoCs. Therefore, in this paper, we introduce a novel unconventional transfer learning approach to train a new ML model by extracting and using weights from an existing ML model running on an embedded SoC without having access to the model within the SoC. Our approach captures power consumption measurements from the SoC while it is executing the ML model and translates them to an approximated weights matrix used to initialize the new ML model. This improves the learning efficiency and predictive performance of the new model, especially in scenarios with limited data available to train the model. Our novel approach can effectively increase the accuracy of the new ML model up to 3 times compared to classical training methods using the same amount of limited training data.