A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank

• URL: http://arxiv.org/abs/2111.15179v2
• Date: Wed, 1 Dec 2021 01:52:01 GMT
• Title: A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank
• Authors: Moonjung Eo, Suhyun Kang, Wonjong Rhee
• Abstract summary: 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.
• Score: 3.0938904602244355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compression has emerged as one of the essential deep learning research topics, especially for the edge devices that have limited computation power and storage capacity. Among the main compression techniques, low-rank compression via matrix factorization has been known to have two problems. First, an extensive tuning is required. Second, the resulting compression performance is typically not impressive. In this work, 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 resulting BSR (Beam-search and Stable Rank) algorithm requires only a single hyperparameter to be tuned for the desired compression ratio. 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. Furthermore, BSR can perform on par with or better than the state-of-the-art structured pruning methods. As with pruning, BSR can be easily combined with quantization for an additional compression.

Related papers

• LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy [59.1298692559785]
  Key-Value ( KV) cache is crucial component in serving transformer-based autoregressive large language models (LLMs) Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages; (2) KV cache compression at test time; and (3) KV cache compression at test time. We propose a low-rank approximation of KV weight matrices, allowing plug-in integration with existing transformer-based LLMs without model retraining. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages.
  arXiv  Detail & Related papers  (2024-10-04T03:10:53Z)
• HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression [6.0163252984457145]
  Hyperspectral Compression Transformer (HyCoT) is a transformer-based autoencoder for pixelwise HSI compression. Experimental results on the HySpecNet-11k dataset demonstrate that HyCoT surpasses the state of the art across various compression ratios by over 1 dB of PSNR.
  arXiv  Detail & Related papers  (2024-08-16T12:27:46Z)
• UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation [59.3877309501938]
  Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios. We introduce a codebook containing frequency domain information as a prior input to the INR network. This enhances the representational power of INR and provides distinctive conditioning for different image blocks.
  arXiv  Detail & Related papers  (2024-05-27T05:52:13Z)
• Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN [5.25545980258284]
  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.
  arXiv  Detail & Related papers  (2024-03-26T07:26:00Z)
• Lossy and Lossless (L$^2$) Post-training Model Size Compression [12.926354646945397]
  We propose a post-training model size compression method that combines lossy and lossless compression in a unified way. Our method can achieve a stable $10times$ compression ratio without sacrificing accuracy and a $20times$ compression ratio with minor accuracy loss in a short time.
  arXiv  Detail & Related papers  (2023-08-08T14:10:16Z)
• DiffRate : Differentiable Compression Rate for Efficient Vision Transformers [98.33906104846386]
  Token compression aims to speed up large-scale vision transformers (e.g. ViTs) by pruning (dropping) or merging tokens. DiffRate is a novel token compression method that has several appealing properties prior arts do not have.
  arXiv  Detail & Related papers  (2023-05-29T10:15:19Z)
• 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)
• Implicit Neural Representations for Image Compression [103.78615661013623]
  Implicit Neural Representations (INRs) have gained attention as a novel and effective representation for various data types. We propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding. We find that our approach to source compression with INRs vastly outperforms similar prior work.
  arXiv  Detail & Related papers  (2021-12-08T13:02:53Z)
• 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)
• 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.