Machine Learning at the Edge: Difference between revisions
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===The Need for Model Optimization at the Edge=== | ===The Need for Model Optimization at the Edge=== | ||
Given the constrained resources, along with the inherently dynamic environment that edge devices must function in, model optimization is a crucial part of machine learning in edge computing. | Given the constrained resources, along with the inherently dynamic environment that edge devices must function in, model optimization is a crucial part of machine learning in edge computing. The current most widely used methodology consists of simply specifying an exceptionally large set of parameters, and giving it to the model to train on. This can be feasible when hardware is very advanced and powerful, and is necessary for systems such as Large Language Models (LLMs). However, this is no longer viable when dealing with the devices and environments at the edge. It is crucial to identify the best parameters and training methodology so as to minimize the amount of work done by these devices, while compromising as little as possible on the accuracy of the models. | ||
Revision as of 22:53, 4 April 2025
Machine Learning at the Edge
4.1 Overview of ML at the Edge
4.2 ML Training at the Edge
4.3 ML Model Optimization at the Edge
The Need for Model Optimization at the Edge
Given the constrained resources, along with the inherently dynamic environment that edge devices must function in, model optimization is a crucial part of machine learning in edge computing. The current most widely used methodology consists of simply specifying an exceptionally large set of parameters, and giving it to the model to train on. This can be feasible when hardware is very advanced and powerful, and is necessary for systems such as Large Language Models (LLMs). However, this is no longer viable when dealing with the devices and environments at the edge. It is crucial to identify the best parameters and training methodology so as to minimize the amount of work done by these devices, while compromising as little as possible on the accuracy of the models.