Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks
Machine Learning
2020-12-23 v2 Optimization and Control
Machine Learning
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.
Cite
@article{arxiv.2008.11117,
title = {Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks},
author = {Jonathan Ashbrock and Alexander M. Powell},
journal= {arXiv preprint arXiv:2008.11117},
year = {2020}
}
Comments
19 pages, 2 figures