May 13, 2024, 4:41 a.m. | 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

cs.LG updates on

arXiv:2405.06038v1 Announce Type: new
Abstract: Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory, energy, and computation. To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning. Recently, there has been a surge in research of compression methods to achieve model efficiency while retaining the performance. Furthermore, more and more works focus on customizing …

abstract algorithm artificial artificial intelligence arxiv challenges compression computation cost cs.lg deployment energy hardware however intelligence memory networks neural networks researchers safe survey tasks them type

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