Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN

• URL: http://arxiv.org/abs/2403.17447v2
• Date: Sat, 17 Aug 2024 09:20:42 GMT
• Title: Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN
• Authors: Yingtao Shen, Minqing Sun, Jianzhe Lin, Jie Zhao, An Zou,
• Abstract summary: We propose a systematic and optimal sequence to apply multiple compression techniques in the most effective order. Our proposed Order of Compression significantly reduces computational costs by up to 859 times on ResNet34, with negligible accuracy loss. We believe our simple yet effective exploration of the order of compression will shed light on the practice of model compression.
• Score: 5.25545980258284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model compression has gained significant popularity as a means to alleviate the computational and memory demands of machine learning models. Each compression technique leverages unique features to reduce the size of neural networks. Although intuitively combining different techniques may enhance compression effectiveness, we find that the order in which they are combined significantly influences performance. To identify the optimal sequence for compressing neural networks, we propose the Order of Compression, a systematic and optimal sequence to apply multiple compression techniques in the most effective order. We start by building the foundations of the orders between any two compression approaches and then demonstrate inserting additional compression between any two compressions will not break the order of the two compression approaches. Based on the foundations, an optimal order is obtained with topological sorting. Validated on image-based regression and classification networks across different datasets, our proposed Order of Compression significantly reduces computational costs by up to 859 times on ResNet34, with negligible accuracy loss (-0.09% for CIFAR10) compared to the baseline model. We believe our simple yet effective exploration of the order of compression will shed light on the practice of model compression.

Related papers

• AlphaZip: Neural Network-Enhanced Lossless Text Compression [0.0]
  This paper introduces a lossless text compression approach using a Large Language Model (LLM) The method involves two key steps: first, prediction using a dense neural network architecture, such as a transformer block; second, compressing the predicted ranks with standard compression algorithms like Adaptive Huffman, LZ77, or Gzip.
  arXiv  Detail & Related papers  (2024-09-23T14:21:06Z)
• Learning Accurate Performance Predictors for Ultrafast Automated Model Compression [86.22294249097203]
  We propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost.
  arXiv  Detail & Related papers  (2023-04-13T10:52:49Z)
• Towards Hardware-Specific Automatic Compression of Neural Networks [0.0]
  pruning and quantization are the major approaches to compress neural networks nowadays. Effective compression policies consider the influence of the specific hardware architecture on the used compression methods. We propose an algorithmic framework called Galen to search such policies using reinforcement learning utilizing pruning and quantization.
  arXiv  Detail & Related papers  (2022-12-15T13:34:02Z)
• Estimating the Resize Parameter in End-to-end Learned Image Compression [50.20567320015102]
  We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our results show that our new resizing parameter estimation framework can provide Bjontegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
  arXiv  Detail & Related papers  (2022-04-26T01:35:02Z)
• A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank [3.0938904602244355]
  We propose a low-rank compression method that utilizes a modified beam-search for an automatic rank selection and a modified stable rank for a compression-friendly training. The performance of BSR in terms of accuracy and compression ratio trade-off curve turns out to be superior to the previously known low-rank compression methods.
  arXiv  Detail & Related papers  (2021-11-30T07:36:23Z)
• Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition [62.41259783906452]
  We present a novel global compression framework for deep neural networks. It automatically analyzes each layer to identify the optimal per-layer compression ratio. Our results open up new avenues for future research into the global performance-size trade-offs of modern neural networks.
  arXiv  Detail & Related papers  (2021-07-23T20:01:30Z)
• Towards Compact CNNs via Collaborative Compression [166.86915086497433]
  We propose a Collaborative Compression scheme, which joints channel pruning and tensor decomposition to compress CNN models. We achieve 52.9% FLOPs reduction by removing 48.4% parameters on ResNet-50 with only a Top-1 accuracy drop of 0.56% on ImageNet 2012.
  arXiv  Detail & Related papers  (2021-05-24T12:07:38Z)
• Neural Network Compression Via Sparse Optimization [23.184290795230897]
  We propose a model compression framework based on the recent progress on sparse optimization. We achieve up to 7.2 and 2.9 times FLOPs reduction with the same level of evaluation of accuracy on VGG16 for CIFAR10 and ResNet50 for ImageNet.
  arXiv  Detail & Related papers  (2020-11-10T03:03:55Z)
• GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework [94.26938614206689]
  We propose the first unified optimization framework combining multiple compression means for GAN compression, dubbed GAN Slimming. We apply GS to compress CartoonGAN, a state-of-the-art style transfer network, by up to 47 times, with minimal visual quality degradation.
  arXiv  Detail & Related papers  (2020-08-25T14:39:42Z)
• PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning [62.440827696638664]
  We introduce a simple algorithm that directly compresses the model differences between neighboring workers. Inspired by the PowerSGD for centralized deep learning, this algorithm uses power steps to maximize the information transferred per bit.
  arXiv  Detail & Related papers  (2020-08-04T09:14:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.