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Recent advances in self-supervised learning and the Transformer architecture have significantly improved natural language processing (NLP), achieving remarkably low perplexity.
LUT filters for quantized processing of signals
R.L. de Queiroz and P.A. Stein · 2004
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LUT optimization for memory-based computation
Pramod Kumar Meher · 2010
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Low-rank matrix factorization for deep neural network training with high-dimensional output targets
Tara N. Sainath, Brian Kingsbury, Vikas Sindhwani, Ebru Arisoy, and Bhuvana Ramabhadran · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Reliability lessons learned from gpu experience with the titan supercomputer at oak ridge leadership computing facility
Devesh Tiwari, Saurabh Gupta, George Gallarno, Jim Rogers, and Don Maxwell · 2015
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Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
Song Han, Huizi Mao, and William J. Dally · 2016
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Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 2016
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The lambada dataset, August 2016
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández · 2016
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XNOR-Net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Towards the limit of network quantization
Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee · 2017
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8-bit inference with TensorRT
Szymon Migacz · 2017
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Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos, and Tijmen Blankevoort · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Scalpel: Customizing DNN pruning to the underlying hardware parallelism
Jiecao Yu, Andrew Lukefahr, David Palframan, Ganesh Dasika, Reetuparna Das, and Scott Mahlke · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
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GroupReduce: Block-wise low-rank approximation for neural language model shrinking
Patrick Chen, Si Si, Yang Li, Ciprian Chelba, and Cho-Jui Hsieh · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Nvidia tensor core programmability, performance & precision
Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, and Jeffrey S Vetter · 2018
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Training wide residual networks for deployment using a single bit for each weight
Mark D. McDonnell · 2018
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Model compression via distillation and quantization
Antonio Polino, Razvan Pascanu, and Dan Alistarh · 2018
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Training and inference with integers in deep neural networks
Shuang Wu, Guoqi Li, Feng Chen, and Luping Shi · 2018
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Alternating multi-bit quantization for recurrent neural networks
Chen Xu, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, and Hongbin Zha · 2018
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Efficient 8-bit quantization of transformer neural machine language translation model
Aishwarya Bhandare, Vamsi Sripathi, Deepthi Karkada, Vivek Menon, Sun Choi, Kushal Datta, and Vikram Saletore · 2019
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, and Geoffrey Irving · 2021
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Multiplication through a single look-up-table (LUT) in CNN inference computation
Shiyu Xu, Qi Wang, Xingbo Wang, Shihang Wang, and Terry Tao Ye · 2021
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Palm: Scaling language modeling with pathways, 2022
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel · 2022
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
Cited alongside, same era.
Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
Cited alongside, same era.
Evaluation of power controls and counters on general-purpose graphics processing units (gpus)
Ghazanfar Ali, Sridutt Bhalachandra, Nicholas Wright, Alan Sill, and Yong Chen · 2020
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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A simple framework for contrastive learning of visual representations
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