Summary of Comprehensive Study on Performance Evaluation and Optimization Of Model Compression: Bridging Traditional Deep Learning and Large Language Models, by Aayush Saxena et al.
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models
by Aayush Saxena, Arit Kumar Bishwas, Ayush Ashok Mishra, Ryan Armstrong
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning models have achieved significant success across various industries, but their increasing size and energy requirements make deployment on low-compute devices challenging. To address this issue, researchers have proposed several solutions to reduce model complexity, including weight quantization, parameter pruning, network pruning, low-rank representation, weights sharing, neural architecture search, and knowledge distillation. This paper investigates the performance impacts of compressing popular deep learning models for image classification, object detection, language models, and generative models using quantization and pruning techniques. The study implements both compression methods on various large language models and evaluates their performance using standard metrics (model size, accuracy, and inference time). The findings are discussed in the context of challenges and future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models have helped many industries succeed, but they require a lot of energy to run. This makes it hard to use them on devices with low power and computing capacity. To fix this problem, researchers have come up with several ideas to make these models smaller and more efficient. In this study, scientists tested how well popular deep learning models work when compressed using two different methods: quantization and pruning. They applied these methods to various language models and evaluated their performance based on size, accuracy, and speed. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Inference » Knowledge distillation » Object detection » Pruning » Quantization
