MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning

• URL: http://arxiv.org/abs/2408.13482v2
• Date: Tue, 3 Sep 2024 00:48:37 GMT
• Title: MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning
• Authors: Seungbeom Hu, ChanJun Park, Andrew Ferraiuolo, Sang-Ki Ko, Jinwoo Kim, Haein Song, Jieung Kim,
• Abstract summary: Pruning is a well-established technique that reduces the size of neural networks while mathematically guaranteeing accuracy preservation. We develop a new pruning algorithm, MPruner, that leverages mutual information through vector similarity. MPruner achieved up to a 50% reduction in parameters and memory usage for CNN and transformer-based models, with minimal to no loss in accuracy.
• Score: 7.262751938473306
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Determining the optimal size of a neural network is critical, as it directly impacts runtime performance and memory usage. Pruning is a well-established model compression technique that reduces the size of neural networks while mathematically guaranteeing accuracy preservation. However, many recent pruning methods overlook the global contributions of individual model components, making it difficult to ensure that a pruned model meets the desired dataset and performance requirements. To address these challenges, we developed a new pruning algorithm, MPruner, that leverages mutual information through vector similarity. MPruner utilizes layer clustering with the Centered Kernel Alignment (CKA) similarity metric, allowing us to incorporate global information from the neural network for more precise and efficient layer-wise pruning. We evaluated MPruner across various architectures and configurations, demonstrating its versatility and providing practical guidelines. MPruner achieved up to a 50% reduction in parameters and memory usage for CNN and transformer-based models, with minimal to no loss in accuracy.

Related papers

• SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models [19.479746878680707]
  Layer pruning is a potent approach to reduce network size and improve computational efficiency. We propose a Similarity Guided fast Layer Partition pruning for compressing large deep models. Our method outperforms the state-of-the-art methods in both accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-10-14T04:01:08Z)
• SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer [12.717450255837178]
  Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency. The most advanced SNN, Spikformer, combines the self-attention module from Transformer with SNN to achieve remarkable performance. We present SparseSpikformer, a co-design framework aimed at achieving sparsity in Spikformer through token and weight pruning techniques.
  arXiv  Detail & Related papers  (2023-11-15T09:22:52Z)
• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• Prompt Tuning for Parameter-efficient Medical Image Segmentation [79.09285179181225]
  We propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. We pre-train this architecture with a dedicated dense self-supervision scheme based on assignments to online generated prototypes. We demonstrate that the resulting neural network model is able to attenuate the gap between fully fine-tuned and parameter-efficiently adapted models.
  arXiv  Detail & Related papers  (2022-11-16T21:55:05Z)
• NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction [37.357949900603295]
  We propose a neural architecture representation model that can be used to estimate attributes holistically. Experiment results show that our proposed framework can be used to predict the latency and accuracy attributes of both cell architectures and whole deep neural networks.
  arXiv  Detail & Related papers  (2022-11-15T10:15:21Z)
• Compact representations of convolutional neural networks via weight pruning and quantization [63.417651529192014]
  We propose a novel storage format for convolutional neural networks (CNNs) based on source coding and leveraging both weight pruning and quantization. We achieve a reduction of space occupancy up to 0.6% on fully connected layers and 5.44% on the whole network, while performing at least as competitive as the baseline.
  arXiv  Detail & Related papers  (2021-08-28T20:39:54Z)
• Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities [80.06422693778141]
  We propose a flexible new pruning mechanism that facilitates pruning at different granularities (weights, kernels, filters/feature maps) We refer to this algorithm as Dynamic Probabilistic Pruning (DPP) We show that DPP achieves competitive compression rates and classification accuracy when pruning common deep learning models trained on different benchmark datasets for image classification.
  arXiv  Detail & Related papers  (2021-05-26T17:01:52Z)
• Solving Mixed Integer Programs Using Neural Networks [57.683491412480635]
  This paper applies learning to the two key sub-tasks of a MIP solver, generating a high-quality joint variable assignment, and bounding the gap in objective value between that assignment and an optimal one. Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP. We evaluate our approach on six diverse real-world datasets, including two Google production datasets and MIPLIB, by training separate neural networks on each.
  arXiv  Detail & Related papers  (2020-12-23T09:33:11Z)
• Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming [11.712073757744452]
  We introduce a mixed integer program (MIP) for assigning importance scores to each neuron in deep neural network architectures. We drive the solver to minimize the number of critical neurons (i.e., with high importance score) that need to be kept for maintaining the overall accuracy of the trained neural network.
  arXiv  Detail & Related papers  (2020-02-17T21:32:47Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

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.