Physics Inspired Criterion for Pruning-Quantization Joint Learning

• URL: http://arxiv.org/abs/2312.00851v2
• Date: Tue, 4 Jun 2024 07:34:05 GMT
• Title: Physics Inspired Criterion for Pruning-Quantization Joint Learning
• Abstract summary: We propose a novel physics inspired criterion for pruning-quantization joint learning (PIC-PQ) We establish a linear relationship between the filters' importance distribution and the filter property (FP) by a learnable scale deformation in the physics inspired criterion (PIC) Experiments on benchmarks of image classification demonstrate that PIC-PQ yields a good trade-off between accuracy and bit-operations (BOPs) compression ratio.
• Score: 30.437300158693795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pruning-quantization joint learning always facilitates the deployment of deep neural networks (DNNs) on resource-constrained edge devices. However, most existing methods do not jointly learn a global criterion for pruning and quantization in an interpretable way. In this paper, we propose a novel physics inspired criterion for pruning-quantization joint learning (PIC-PQ), which is explored from an analogy we first draw between elasticity dynamics (ED) and model compression (MC). Specifically, derived from Hooke's law in ED, we establish a linear relationship between the filters' importance distribution and the filter property (FP) by a learnable deformation scale in the physics inspired criterion (PIC). Furthermore, we extend PIC with a relative shift variable for a global view. To ensure feasibility and flexibility, available maximum bitwidth and penalty factor are introduced in quantization bitwidth assignment. Experiments on benchmarks of image classification demonstrate that PIC-PQ yields a good trade-off between accuracy and bit-operations (BOPs) compression ratio e.g., 54.96X BOPs compression ratio in ResNet56 on CIFAR10 with 0.10% accuracy drop and 53.24X in ResNet18 on ImageNet with 0.61% accuracy drop). The code will be available at https://github.com/fanxxxxyi/PIC-PQ.

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