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 this https URL

通过将弹性动力学与模型压缩进行类比，提出了一个新颖的物理启发准则，用于修剪 - 量化联合学习，实现在资源受限的边缘设备上部署深度神经网络。实验证明，该准则在精度和位操作（BOPs）压缩比之间取得了良好的折中，例如，在 CIFAR10 上对 ResNet56 的位操作压缩比为 54.96 倍，并且只有 0.10％的精度下降。