It has always been the aim of the MAGEEC project to be as open as is practically possible, by making research outputs, software sources and hardware designs all freely available. Of course, hardware still needs to be manufactured and if you don’t have access to facilities for doing this the setup costs associated with third party services can be prohibitive for just one or a few boards.
As part of the MAGEEC we need an energy measurement infrastructure for a couple things: taking measurements for the machine learner to train on, and measurements to evaluate the decisions that the machine learner has made. Furthermore, we want to be able to do this over many platforms, with minimal human interaction and in a convenient way. This leads to the creation of a hardware platform, and a software framework to support it and that allows for easy interaction with the board.
My name is Ashley Whetter, I’m a second year computer science student at Bristol University and I’m working on the energy measurement hardware infrastructure.
As a machine learner, the MAGEEC plugin needs initial training data so that it has an idea of what optimizations will result in a more energy efficient program, given a set of program features.
Using embedded platforms and custom energy monitoring hardware we can accurately measure the amount of energy that a program uses. By automating the loading, running, and measuring of a set of benchmarks, we can easily collect a set of training data for the MAGEEC plugin.
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