UPSCALE: Advanced Channel Pruning without Constraints – Apple’s Cutting-Edge Machine Learning Research
Introduction:
Modern neural networks are becoming increasingly large and complex, leading to longer inference times. To address this issue, channel pruning has emerged as an effective compression technique that reduces resource consumption by removing channels from convolutional weights. However, when dealing with multi-branch segments of a model, removing channels can result in additional memory copies during inference, causing even slower performance than the unpruned model. Existing pruning methods have attempted to resolve this by constraining certain channels to be pruned together, but these constraints lead to a significant loss in accuracy. To overcome these challenges, we propose an innovative approach called the Unconstrained Channel Pruning with reOrdering (UCPE) algorithm, which allows for unconstrained pruning by rearranging channels to minimize memory copies. Not only does UCPE improve ImageNet top-1 accuracy for post-training pruning by an average of 2.1 points, but it also reduces latency by up to 52.8% compared to naive unconstrained pruning, effectively eliminating memory copies during inference. This pioneering algorithm benefits a range of models such as DenseNet, EfficientNetV2, and ResNet, making it a valuable tool for efficient and accurate model pruning.
Full Article: UPSCALE: Advanced Channel Pruning without Constraints – Apple’s Cutting-Edge Machine Learning Research
Channel pruning is an effective technique used to reduce the size and complexity of modern neural networks. However, it can also increase inference time, posing a challenge for efficient model deployment. A new solution has been developed to address this issue by enabling unconstrained pruning through channel reordering.
In the case of multi-branch segments in a model, removing channels can introduce additional memory copies during inference, resulting in increased latency. Existing pruning methods have attempted to overcome this by imposing constraints on which channels should be pruned together. While this eliminates memory copies at inference time, it significantly affects the accuracy of the pruned model.
The newly developed algorithm, called UCPE (Unconstrained Channel Pruning with channel reordering), provides a generic solution for pruning models with any pruning pattern. By removing constraints from existing pruning heuristics, UCPE improves the accuracy of post-training pruning on ImageNet by an average of 2.1 points. This improvement benefits various models, including DenseNet (+16.9), EfficientNetV2 (+7.9), and ResNet (+6.2).
In addition to boosting accuracy, the UCPE algorithm also reduces latency during inference. Compared to naive unconstrained pruning, UCPE achieves a latency reduction of up to 52.8%. This significant improvement nearly eliminates memory copies at inference time, further optimizing the efficiency of pruned models.
Overall, the UCPE algorithm offers a solution to the challenges posed by channel pruning in modern neural networks. It allows for unconstrained pruning through channel reordering, improving both accuracy and efficiency in model deployment. By minimizing memory copies and reducing latency, UCPE enhances the performance of pruned models, making them more suitable for real-world applications.
Summary: UPSCALE: Advanced Channel Pruning without Constraints – Apple’s Cutting-Edge Machine Learning Research
Modern neural networks continue to grow in size and complexity, leading to longer inference times. Channel pruning, a useful compression technique, addresses this issue by removing channels from convolutional weights to reduce resource consumption. However, pruning multi-branch segments introduces additional memory copies during inference, resulting in increased latency. Existing pruning methods impose constraints to eliminate these copies, but they negatively impact accuracy. To overcome this challenge, we propose unconstrained pruning by reordering channels to minimize memory copies. Our UCPE algorithm improves ImageNet top-1 accuracy for post-training pruning by 2.1 points on average, benefiting popular models like DenseNet, EfficientNetV2, and ResNet. Additionally, UCPE reduces latency by up to 52.8%, almost entirely eliminating memory copies during inference.
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