Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantization

• URL: http://arxiv.org/abs/2309.13575v3
• Date: Tue, 3 Oct 2023 19:53:34 GMT
• Title: Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantization
• Authors: Christopher Subia-Waud and Srinandan Dasmahapatra
• Abstract summary: Weight-sharing quantization has emerged as a technique to reduce energy expenditure during inference in large neural networks. This paper proposes a probabilistic framework based on Bayesian neural networks (BNNs) and a variational relaxation to identify which weights can be moved to which cluster centre. Our method outperforms the state-of-the-art quantization method top-1 accuracy by 1.6% on ImageNet using DeiT-Tiny.
• Score: 7.2282857478457805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weight-sharing quantization has emerged as a technique to reduce energy expenditure during inference in large neural networks by constraining their weights to a limited set of values. However, existing methods for weight-sharing quantization often make assumptions about the treatment of weights based on value alone that neglect the unique role weight position plays. This paper proposes a probabilistic framework based on Bayesian neural networks (BNNs) and a variational relaxation to identify which weights can be moved to which cluster centre and to what degree based on their individual position-specific learned uncertainty distributions. We introduce a new initialisation setting and a regularisation term which allow for the training of BNNs under complex dataset-model combinations. By leveraging the flexibility of weight values captured through a probability distribution, we enhance noise resilience and downstream compressibility. Our iterative clustering procedure demonstrates superior compressibility and higher accuracy compared to state-of-the-art methods on both ResNet models and the more complex transformer-based architectures. In particular, our method outperforms the state-of-the-art quantization method top-1 accuracy by 1.6% on ImageNet using DeiT-Tiny, with its 5 million+ weights now represented by only 296 unique values.

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