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arXiv · 1907.07220

Learning Multimodal Fixed-Point Weights using Gradient Descent

Lukas Enderich·Fabian Timm·Lars Rosenbaum·Wolfram Burgard
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Abstract

Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-point quantization, we propose a gradient-based optimization strategy to generate a symmetric mixture of Gaussian modes (SGM) where each mode belongs to a particular quantization stage. We achieve 2-bit state-of-the-art performance and illustrate the model's ability for self-dependent weight adaptation during training.

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Lukas Enderich, Fabian Timm, Lars Rosenbaum, Wolfram Burgard. 2019-07-16. Learning Multimodal Fixed-Point Weights using Gradient Descent. https://arxiv.org/abs/1907.07220

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