Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks

• URL: http://arxiv.org/abs/2008.11117v2
• Date: Tue, 22 Dec 2020 15:48:20 GMT
• Title: Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks
• Authors: Jonathan Ashbrock, Alexander M. Powell
• Abstract summary: We introduce Gradient Markov Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.
• Score: 77.34726150561087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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