MIT boffins cram ML training into microcontroller memory
Researchers claim to have developed techniques to enable the training of a machine learning model using less than a quarter of a megabyte of memory, making it suitable for operation in microcontrollers and other edge hardware with limited resources.
The techniques can be used to train a machine learning model on a microcontroller in a matter of minutes, it is claimed, and they have produced a paper on the subject, titled"According to the authors, on-device training of a model will enable it to adapt in response to new data collected by the device's sensors. By training and adapting locally at the edge, the model can learn to continuously improve its predictions for the life of the application.
Meanwhile, deep learning training systems like PyTorch and TensorFlow are often run on clusters of servers with gigabytes of memory at their disposal, and while there are edge deep learning inference frameworks, some of these lack support for the back-propagation to adjust the models. This is no mean feat, since training a typical deep learning model undergoes hundreds of updates as it learns, and because there may be millions of weights and activations involved, training a model requires much more memory than running a pre-trained model.
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