Summary of Effective Interplay Between Sparsity and Quantization: From Theory to Practice, by Simla Burcu Harma et al.
Effective Interplay between Sparsity and Quantization: From Theory to Practice
by Simla Burcu Harma, Ayan Chakraborty, Elizaveta Kostenok, Danila Mishin, Dongho Ha, Babak Falsafi, Martin Jaggi, Ming Liu, Yunho Oh, Suvinay Subramanian, Amir Yazdanbakhsh
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates the interactions between two prominent deep neural network (DNN) compression methods: sparsity and quantization. Researchers have previously shown that these techniques can significantly reduce DNNs’ computational and memory footprints while preserving model accuracy. However, the paper reveals that when combined, these methods are not orthogonal, meaning their cumulative impact on model accuracy is greater than the sum of their individual effects. The authors provide mathematical proofs and experimental results using large language models like OPT and LLaMA, as well as vision models like ViT and ResNet, to demonstrate that the order in which these methods are applied matters. They also show that compounded errors from sparsity and quantization can harm accuracy, highlighting best practices for deploying compressed DNNs on resource-constrained platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how two techniques used to shrink deep neural networks (DNNs) work together. Sparsity and quantization help reduce the size of these complex models, making them more efficient for use in devices with limited resources. Researchers thought that using both methods wouldn’t cause extra problems because they worked independently. However, this study shows that’s not true. It reveals mathematically and through experiments on different types of DNNs (like those used for language or images) that the order matters when combining these techniques. The results also show that errors can add up and affect accuracy. This research aims to help developers make their models more efficient without sacrificing performance. |
Keywords
» Artificial intelligence » Llama » Neural network » Quantization » Resnet » Vit
