Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks

Jonathan Ashbrock; Alexander M. Powell

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arXiv · 2008.11117

Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks

Jonathan Ashbrock·Alexander M. Powell

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Abstract

The massive size of modern neural networks has motivated substantial recent interest in neural network quantization. We introduce Stochastic Markov Gradient Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. The SMGD algorithm is designed for settings where memory is highly constrained during training. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.

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Jonathan Ashbrock, Alexander M. Powell. 2020-08-25. Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks. https://arxiv.org/abs/2008.11117

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