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Training machine learning models on mobile devices has the potential ofimproving both privacy and accuracy of the models. However, one of the majorobstacles to achieving this goal is the memory limitation of mobile devices.Reducing training memory enables models with high-dimensional weight matrices,like automatic speech recognition (ASR) models, to be trained on-device. Inthis paper, we propose approximating the gradient matrices of deep neuralnetworks using a low-rank parameterization as an avenue to save trainingmemory. The low-rank gradient approximation enables more advanced,memory-intensive optimization techniques to be run on device. Our experimentalresults show that we can reduce the training memory by about 33.0% for Adamoptimization. It uses comparable memory to momentum optimization and achieves a4.5% relative lower word error rate on an ASR personalization task.

Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network