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Deploying large language models (LLMs) on edge devices presents significant challenges due to the substantial computational overhead and memory requirements.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 1912
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Optimal brain damage
Yann LeCun, John S. Denker, and Sara A. Solla. 1989 · 1989
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G. Stork. 1992 · 1992
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GLU variants improve transformer
Noam Shazeer. 2020 · 2002
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V. Le. 2018 · 2018
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Earlier work this paper cites.
Optimal brain compression: A framework for accurate post-training quantization and pruning
Elias Frantar and Dan Alistarh. 2022 · 2022
Earlier work this paper cites.
GPTQ: accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. 2022 · 2022
Cited alongside, same era.
OPT: open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona T. Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
LLM in a flash: Efficient large language model inference with limited memory
Keivan Alizadeh, Seyed-Iman Mirzadeh, Dmitry Belenko, Karen Khatamifard, Minsik Cho, Carlo C. del Mundo, Mohammad Rastegari, and Mehrdad Farajtabar. 2023 · 2023
Cited alongside, same era.
Sparsegpt: Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh. 2023 · 2023
Cited alongside, same era.
Distillspec: Improving speculative decoding via knowledge distillation
Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, and Rishabh Agarwal. 2023 · 2023
Later among the works it cites.
Medusa: Simple LLM inference acceleration framework with multiple decoding heads
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, and Tri Dao. 2024 · 2024
Closest in time.
Kvquant: Towards 10 million context length LLM inference with KV cache quantization
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, and Amir Gholami. 2024 · 2024
Closest in time.
CATS: contextually-aware thresholding for sparsity in large language models
Je-Yong Lee, Donghyun Lee, Genghan Zhang, Mo Tiwari, and Azalia Mirhoseini. 2024 · 2024
Closest in time.
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Fast inference from transformers via speculative decoding
Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023 · 2023
Cited alongside, same era.
The lazy neuron phenomenon: On emergence of activation sparsity in transformers
Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix Chern, Felix X. Yu, Ruiqi Guo, and Sanjiv Kumar. 2023 · 2023
Cited alongside, same era.
Deja vu: Contextual sparsity for efficient llms at inference time
Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, and Beidi Chen. 2023 · 2023
Cited alongside, same era.
Powerinfer: Fast large language model serving with a consumer-grade GPU
Yixin Song, Zeyu Mi, Haotong Xie, and Haibo Chen. 2023 · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Cited alongside, same era.
Smoothquant: Accurate and efficient post-training quantization for large language models
Guangxuan Xiao, Ji Lin, Mickaël Seznec, Hao Wu, Julien Demouth, and Song Han. 2023 · 2023
Cited alongside, same era.
AWQ: activation-aware weight quantization for on-device LLM compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. 2024 · 2024
Closest in time.
KIVI: A tuning-free asymmetric 2bit quantization for KV cache
Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. 2024 · 2024
Closest in time.
Relu strikes back: Exploiting activation sparsity in large language models
Seyed-Iman Mirzadeh, Keivan Alizadeh-Vahid, Sachin Mehta, Carlo C. del Mundo, Oncel Tuzel, Golnoosh Samei, Mohammad Rastegari, and Mehrdad Farajtabar. 2024 · 2024
Closest in time.
Prosparse: Introducing and enhancing intrinsic activation sparsity within large language models
Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, and Maosong Sun. 2024 · 2024
Closest in time.
A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J. Zico Kolter. 2024 · 2024
Closest in time.
Atom: Low-bit quantization for efficient and accurate LLM serving
Yilong Zhao, Chien-Yu Lin, Kan Zhu, Zihao Ye, Lequn Chen, Size Zheng, Luis Ceze, Arvind Krishnamurthy, Tianqi Chen, and Baris Kasikci. 2024 · 2024
Closest in time.