Summary of From Algorithm to Hardware: a Survey on Efficient and Safe Deployment Of Deep Neural Networks, by Xue Geng et al.
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
by Xue Geng, Zhe Wang, Chunyun Chen, Qing Xu, Kaixin Xu, Chao Jin, Manas Gupta, Xulei Yang, Zhenghua Chen, Mohamed M. Sabry Aly, Jie Lin, Min Wu, Xiaoli Li
First submitted to arxiv on: 9 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 Deep neural networks have revolutionized artificial intelligence tasks, but their deployment poses significant challenges due to memory, energy, and computation costs. Researchers have developed model compression techniques like quantization and pruning to achieve efficiency while retaining performance. The surge in research on customizing DNN hardware accelerators to leverage these techniques is a notable trend. In addition to efficiency, preserving security and privacy is critical for deploying DNNs. This comprehensive survey aims to provide an overview of recent research toward high-performance, cost-efficient, and safe deployment of DNNs. We cover mainstream model compression techniques like quantization, pruning, knowledge distillation, and optimizations of non-linear operations. We also introduce advances in designing hardware accelerators that adapt to efficient model compression approaches. Homomorphic encryption integration for securing DNN deployment is discussed. Finally, we touch on issues such as hardware evaluation, generalization, and integrating various compression approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks are super smart machines that can do many cool things, but they’re really hard to use because they need a lot of computer power and memory. Some clever people have figured out ways to make them smaller and more efficient without losing their brainpower. This is important for keeping our personal information safe and making sure these super smart machines don’t waste too much energy or take up too much space. This survey looks at all the different ways researchers are trying to solve this problem, from shrinking the models themselves to creating special computer chips that can make them work faster. |
Keywords
» Artificial intelligence » Generalization » Knowledge distillation » Model compression » Pruning » Quantization
