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DNN pruning is a well-liked method to scale back the dimensions of a mannequin, enhance the inference latency, and decrease the facility consumption on DNN accelerators. Nevertheless, current approaches could be too complicated, costly or ineffective to use to quite a lot of imaginative and prescient/language duties, DNN architectures and to honor structured pruning constraints. On this paper, we suggest an environment friendly but efficient train-time pruning scheme, Parameter-free Differentiable Pruning (PDP), which gives state-of-the-art qualities in mannequin dimension, accuracy, and coaching price. PDP makes use of a dynamic perform of weights throughout coaching to generate tender pruning masks for the weights in a parameter-free method for a given pruning goal. Whereas differentiable, the simplicity and effectivity of PDP make it common sufficient to ship state-of-the-art random/structured/channel pruning outcomes on numerous imaginative and prescient and pure language duties. For instance, for MobileNet-v1, PDP can obtain 68.2% top-1 ImageNet1k accuracy at 86.6% sparsity, which is 1.7% larger accuracy than these from the state-of-the-art algorithms. Additionally, PDP yields over 83.1% accuracy on Multi-Style Pure Language Inference with 90% sparsity for BERT, whereas the following finest from the prevailing methods exhibits 81.5% accuracy. As well as, PDP may be utilized to structured pruning, reminiscent of N:M pruning and channel pruning. For 1:4 structured pruning of ResNet18, PDP improved the top-1 ImageNet1k accuracy by over 3.6% over the state-of-the-art. For channel pruning of ResNet50, PDP lowered the top-1 ImageNet1k accuracy by 0.6% from the state-of-the-art.
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