‘reduced-precision NNs’ tag

See Also
Links
Miscellaneous
Link Bibliography

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See Also

Links

“Probing the Decision Boundaries of In-Context Learning in Large Language Models”, Zhao et al 2024

Probing the Decision Boundaries of In-context Learning in Large Language Models

“Scalable Matmul-Free Language Modeling”, Zhu et al 2024

Scalable Matmul-free Language Modeling

“How Good Are Low-Bit Quantized LLaMA-3 Models? An Empirical Study”, Huang et al 2024

How Good Are Low-bit Quantized LLaMA-3 Models? An Empirical Study

“LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models”, Zheng et al 2024

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

“Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression”, Hong et al 2024

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

“The Era of 1-Bit LLMs: All Large Language Models Are in 1.58 Bits”, Ma et al 2024

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

“FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design”, Xia et al 2024

FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design

“Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws”, Sardana & Frankle 2023

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

“TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones”, Yuan et al 2023

TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones

“LLM-FP4: 4-Bit Floating-Point Quantized Transformers”, Liu et al 2023

LLM-FP4: 4-Bit Floating-Point Quantized Transformers

“SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression”, Dettmers et al 2023

SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

“Binary and Ternary Natural Language Generation”, Liu et al 2023

Binary and Ternary Natural Language Generation

“AWQ: Activation-Aware Weight Quantization for LLM Compression and Acceleration”, Lin et al 2023

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

“Big-PERCIVAL: Exploring the Native Use of 64-Bit Posit Arithmetic in Scientific Computing”, Mallasén et al 2023

Big-PERCIVAL: Exploring the Native Use of 64-Bit Posit Arithmetic in Scientific Computing

“Int-4 LLaMa Is Not Enough—Int-3 and Beyond: More Compression, Easier to Build Apps on LLMs That Run Locally”, nolano.org 2023

Int-4 LLaMa is not enough—Int-3 and beyond: More compression, easier to build apps on LLMs that run locally

“SpikeGPT: Generative Pre-Trained Language Model With Spiking Neural Networks”, Zhu et al 2023

SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

“BMT: Binarized Neural Machine Translation”, Zhang et al 2023

BMT: Binarized Neural Machine Translation

“Who Says Elephants Can’t Run: Bringing Large Scale MoE Models into Cloud Scale Production”, Kim et al 2022

Who Says Elephants Can’t Run: Bringing Large Scale MoE Models into Cloud Scale Production

“SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models”, Xiao et al 2022

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

“Efficiently Scaling Transformer Inference”, Pope et al 2022

Efficiently Scaling Transformer Inference

“GPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers”, Frantar et al 2022

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

“Fast DistilBERT on CPUs”, Shen et al 2022

Fast DistilBERT on CPUs

“Broken Neural Scaling Laws”, Caballero et al 2022

Broken Neural Scaling Laws

“GLM-130B: An Open Bilingual Pre-Trained Model”, Zeng et al 2022

GLM-130B: An Open Bilingual Pre-trained Model

“FP8 Formats for Deep Learning”, Micikevicius et al 2022

FP8 Formats for Deep Learning

“`LLM.int8()`: 8-Bit Matrix Multiplication for Transformers at Scale”, Dettmers et al 2022

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

“Is Integer Arithmetic Enough for Deep Learning Training?”, Ghaffari et al 2022

Is Integer Arithmetic Enough for Deep Learning Training?

“On-Device Training Under 256KB Memory”, Lin et al 2022

On-Device Training Under 256KB Memory

“How to Train Accurate BNNs for Embedded Systems?”, Putter & Corporaal 2022

How to train accurate BNNs for embedded systems?

“Director: Deep Hierarchical Planning from Pixels”, Hafner et al 2022

Director: Deep Hierarchical Planning from Pixels

“8-Bit Numerical Formats for Deep Neural Networks”, Noune et al 2022

8-bit Numerical Formats for Deep Neural Networks

“XTC: Extreme Compression for Pre-Trained Transformers Made Simple and Efficient”, Wu et al 2022

XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient

“ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers”, Yao et al 2022

ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

“Matryoshka Representations for Adaptive Deployment”, Kusupati et al 2022

Matryoshka Representations for Adaptive Deployment

“PLAID: An Efficient Engine for Late Interaction Retrieval”, Santhanam et al 2022

PLAID: An Efficient Engine for Late Interaction Retrieval

“Maximizing Communication Efficiency for Large-Scale Training via 0/1 Adam”, Lu et al 2022

Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam

“Is Programmable Overhead Worth The Cost? How Much Do We Pay for a System to Be Programmable? It Depends upon Who You Ask”, Bailey 2022

Is Programmable Overhead Worth The Cost? How much do we pay for a system to be programmable? It depends upon who you ask

“Boosted Dense Retriever”, Lewis et al 2021

Boosted Dense Retriever

“FQ-ViT: Fully Quantized Vision Transformer without Retraining”, Lin et al 2021

FQ-ViT: Fully Quantized Vision Transformer without Retraining

“𝜇NCA: Texture Generation With Ultra-Compact Neural Cellular Automata”, Mordvintsev & Niklasson 2021

𝜇NCA: Texture Generation with Ultra-Compact Neural Cellular Automata

“Prune Once for All: Sparse Pre-Trained Language Models”, Zafrir et al 2021

Prune Once for All: Sparse Pre-Trained Language Models

“8-Bit Optimizers via Block-Wise Quantization”, Dettmers et al 2021

8-bit Optimizers via Block-wise Quantization

“Understanding and Overcoming the Challenges of Efficient Transformer Quantization”, Bondarenko et al 2021

Understanding and Overcoming the Challenges of Efficient Transformer Quantization

“Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better”, Menghani 2021

Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better

“A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness”, Diffenderfer et al 2021

A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness

“Ten Lessons From Three Generations Shaped Google’s TPUv4i”, Jouppi et al 2021

Ten Lessons From Three Generations Shaped Google’s TPUv4i

“High-Performance, Distributed Training of Large-Scale Deep Learning Recommendation Models (DLRMs)”, Mudigere et al 2021

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (DLRMs)

“Deep Residual Learning in Spiking Neural Networks”, Fang et al 2021

Deep Residual Learning in Spiking Neural Networks

“1-Bit Adam: Communication Efficient Large-Scale Training With Adam’s Convergence Speed”, Tang et al 2021

1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed

“ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution”, Song et al 2021

ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution

“Switch Transformers: Scaling to Trillion Parameter Models With Simple and Efficient Sparsity”, Fedus et al 2021

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

“A Primer in BERTology: What We Know about How BERT Works”, Rogers et al 2020

A Primer in BERTology: What we know about how BERT works

“L2L: Training Large Neural Networks With Constant Memory Using a New Execution Algorithm”, Pudipeddi et al 2020

L2L: Training Large Neural Networks with Constant Memory using a New Execution Algorithm

“RegDeepDanbooru: Yet Another Deep Danbooru Project”, zyddnys 2020

RegDeepDanbooru: Yet another Deep Danbooru project

“TernaryBERT: Distillation-Aware Ultra-Low Bit BERT”, Zhang et al 2020

TernaryBERT: Distillation-aware Ultra-low Bit BERT

“HOBFLOPS CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks”, Garland & Gregg 2020

HOBFLOPS CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks

“Bayesian Bits: Unifying Quantization and Pruning”, Baalen et al 2020

Bayesian Bits: Unifying Quantization and Pruning

“General Purpose Text Embeddings from Pre-Trained Language Models for Scalable Inference”, Du et al 2020

General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference

“Lite Transformer With Long-Short Range Attention”, Wu et al 2020

Lite Transformer with Long-Short Range Attention

“Training With Quantization Noise for Extreme Model Compression”, Fan et al 2020

Training with Quantization Noise for Extreme Model Compression

“Moniqua: Modulo Quantized Communication in Decentralized SGD”, Lu & Sa 2020

Moniqua: Modulo Quantized Communication in Decentralized SGD

“Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers”, Li et al 2020

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

“SWAT: Sparse Weight Activation Training”, Raihan & Aamodt 2020

SWAT: Sparse Weight Activation Training

“QUARL: Quantized Reinforcement Learning (ActorQ)”, Lam et al 2019

QUARL: Quantized Reinforcement Learning (ActorQ)

“SCaNN: Accelerating Large-Scale Inference With Anisotropic Vector Quantization”, Guo et al 2019

SCaNN: Accelerating Large-Scale Inference with Anisotropic Vector Quantization

“And the Bit Goes Down: Revisiting the Quantization of Neural Networks”, Stock et al 2019

And the Bit Goes Down: Revisiting the Quantization of Neural Networks

“Surrogate Gradient Learning in Spiking Neural Networks”, Neftci et al 2019

Surrogate Gradient Learning in Spiking Neural Networks

“Rethinking Floating Point for Deep Learning”, Johnson 2018

Rethinking floating point for deep learning

“Learning Recurrent Binary/Ternary Weights”, Ardakani et al 2018

Learning Recurrent Binary/Ternary Weights

“Rethinking Numerical Representations for Deep Neural Networks”, Hill et al 2018

Rethinking Numerical Representations for Deep Neural Networks

“Highly Scalable Deep Learning Training System With Mixed-Precision: Training ImageNet in 4 Minutes”, Jia et al 2018

Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in 4 Minutes

“Quantization Mimic: Towards Very Tiny CNN for Object Detection”, Wei et al 2018

Quantization Mimic: Towards Very Tiny CNN for Object Detection

“Training Imagenet in 3 Hours for $25; and CIFAR-10 for $0.26”, Howard 2018

Training Imagenet in 3 hours for $25; and CIFAR-10 for $0.26

“High-Accuracy Low-Precision Training”, Sa et al 2018

High-Accuracy Low-Precision Training

“Training Wide Residual Networks for Deployment Using a Single Bit for Each Weight”, McDonnell 2018

Training wide residual networks for deployment using a single bit for each weight

“Universal Deep Neural Network Compression”, Choi et al 2018

Universal Deep Neural Network Compression

“Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training”, Lin et al 2017

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

“Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions”, Wu et al 2017

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

“Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method”, Sun et al 2017

Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

“Compressing Word Embeddings via Deep Compositional Code Learning”, Shu & Nakayama 2017

Compressing Word Embeddings via Deep Compositional Code Learning

“Learning Discrete Weights Using the Local Reparameterization Trick”, Shayer et al 2017

Learning Discrete Weights Using the Local Reparameterization Trick

“TensorQuant—A Simulation Toolbox for Deep Neural Network Quantization”, Loroch et al 2017

TensorQuant—A Simulation Toolbox for Deep Neural Network Quantization

“Mixed Precision Training”, Micikevicius et al 2017

Mixed Precision Training

“BitNet: Bit-Regularized Deep Neural Networks”, Raghavan et al 2017

BitNet: Bit-Regularized Deep Neural Networks

“Beating Floating Point at Its Own Game: Posit Arithmetic”, Gustafson & Yonemoto 2017

Beating Floating Point at its Own Game: Posit Arithmetic

“Bolt: Accelerated Data Mining With Fast Vector Compression”, Blalock & Guttag 2017

Bolt: Accelerated Data Mining with Fast Vector Compression

“Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”, Wu et al 2016

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

“Ternary Neural Networks for Resource-Efficient AI Applications”, Alemdar et al 2016

Ternary Neural Networks for Resource-Efficient AI Applications

“Deep Neural Networks Are Robust to Weight Binarization and Other Non-Linear Distortions”, Merolla et al 2016

Deep neural networks are robust to weight binarization and other non-linear distortions

“Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing”, Esser et al 2016

Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing

“XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”, Rastegari et al 2016

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

“Binarized Neural Networks: Training Deep Neural Networks With Weights and Activations Constrained to +1 or −1”, Courbariaux et al 2016

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or −1

“BinaryConnect: Training Deep Neural Networks With Binary Weights during Propagations”, Courbariaux et al 2015

BinaryConnect: Training Deep Neural Networks with binary weights during propagations

“Efficient Supervised Learning in Networks With Binary Synapses”, Baldassi et al 2007

Efficient supervised learning in networks with binary synapses

“A Self-Optimizing, Non-Symmetrical Neural Net for Content Addressable Memory and Pattern Recognition”, Lapedes & Farber 1986

A self-optimizing, non-symmetrical neural net for content addressable memory and pattern recognition

“Building a Vector Database in 2GB for 36 Million Wikipedia Passages”

Building a vector database in 2GB for 36 million Wikipedia passages

Wikipedia

Miscellaneous

Link Bibliography

https://arxiv.org/abs/2406.11233: “Probing the Decision Boundaries of In-Context Learning in Large Language Models”, Siyan Zhao, Tung Nguyen, Aditya Grover

link-bibliography
https://arxiv.org/abs/2404.14047: “How Good Are Low-Bit Quantized LLaMA-3 Models? An Empirical Study”, Wei Huang, Xudong Ma, Haotong Qin, Xingyu Zheng, Chengtao Lv, Hong Chen, Jie Luo, Xiaojuan Qi, Xianglong Liu, Michele Magno

link-bibliography
https://arxiv.org/abs/2402.17764: “The Era of 1-Bit LLMs: All Large Language Models Are in 1.58 Bits”, Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, Furu Wei

link-bibliography
https://arxiv.org/abs/2401.14112#microsoft: “FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design”, Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song

link-bibliography
https://arxiv.org/abs/2312.16862: “TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones”, Zhengqing Yuan, Zhaoxu Li, Lichao Sun

link-bibliography
https://arxiv.org/abs/2310.16836: “LLM-FP4: 4-Bit Floating-Point Quantized Transformers”, Shih-yang Liu, Zechun Liu, Xijie Huang, Pingcheng Dong, Kwang-Ting Cheng

link-bibliography
https://arxiv.org/abs/2305.06946: “Big-PERCIVAL: Exploring the Native Use of 64-Bit Posit Arithmetic in Scientific Computing”, David Mallasén, Alberto A. Del Barrio, Manuel Prieto-Matias

link-bibliography
https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and: “Int-4 LLaMa Is Not Enough—Int-3 and Beyond: More Compression, Easier to Build Apps on LLMs That Run Locally”, nolano.org

link-bibliography
https://arxiv.org/abs/2302.13939: “SpikeGPT: Generative Pre-Trained Language Model With Spiking Neural Networks”, Rui-Jie Zhu, Qihang Zhao, Jason K. Eshraghian

link-bibliography
https://arxiv.org/abs/2302.04907#google: “BMT: Binarized Neural Machine Translation”, Yichi Zhang, Ankush Garg, Yuan Cao, Łukasz Lew, Behrooz Ghorbani, Zhiru Zhang, Orhan Firat

link-bibliography
https://arxiv.org/abs/2211.10438: “SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models”, Guangxuan Xiao, Ji Lin, Mickael Seznec, Julien Demouth, Song Han

link-bibliography
https://arxiv.org/abs/2211.05102#google: “Efficiently Scaling Transformer Inference”, Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Levskaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, Jeff Dean

link-bibliography
https://arxiv.org/abs/2210.17323: “GPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers”, Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh

link-bibliography
https://arxiv.org/abs/2210.02414#baai: “GLM-130B: An Open Bilingual Pre-Trained Model”, Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Peng Zhang, Yuxiao Dong, Jie Tang

link-bibliography
https://arxiv.org/abs/2206.15472: “On-Device Training Under 256KB Memory”, Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, Song Han

link-bibliography
https://arxiv.org/abs/2206.04114#google: “Director: Deep Hierarchical Planning from Pixels”, Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel

link-bibliography
https://arxiv.org/abs/2206.01859#microsoft: “XTC: Extreme Compression for Pre-Trained Transformers Made Simple and Efficient”, Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He

link-bibliography
https://arxiv.org/abs/2206.01861#microsoft: “ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers”, Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He

link-bibliography
https://arxiv.org/abs/2205.13147: “Matryoshka Representations for Adaptive Deployment”, Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi

link-bibliography
https://arxiv.org/abs/2202.06009#microsoft: “Maximizing Communication Efficiency for Large-Scale Training via 0/1 Adam”, Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He

link-bibliography
https://semiengineering.com/is-programmable-overhead-worth-the-cost/: “Is Programmable Overhead Worth The Cost? How Much Do We Pay for a System to Be Programmable? It Depends upon Who You Ask”, Brian Bailey

link-bibliography
https://arxiv.org/abs/2111.13824: “FQ-ViT: Fully Quantized Vision Transformer without Retraining”, Yang Lin, Tianyu Zhang, Peiqin Sun, Zheng Li, Shuchang Zhou

link-bibliography
https://arxiv.org/abs/2111.05754: “Prune Once for All: Sparse Pre-Trained Language Models”, Ofir Zafrir, Ariel Larey, Guy Boudoukh, Haihao Shen, Moshe Wasserblat

link-bibliography
https://arxiv.org/abs/2110.02861: “8-Bit Optimizers via Block-Wise Quantization”, Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer

link-bibliography
https://arxiv.org/abs/2109.12948: “Understanding and Overcoming the Challenges of Efficient Transformer Quantization”, Yelysei Bondarenko, Markus Nagel, Tijmen Blankevoort

link-bibliography
2021-jouppi.pdf: “Ten Lessons From Three Generations Shaped Google’s TPUv4i”, Norman P. Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B. Jablin, George Kurian, James Laudon, Sheng Li, Peter Ma, Xiaoyu Ma, Thomas Norrie, Nishant Patil, Sushma Prasad, Cliff Young, Zongwei Zhou, David Patterson

link-bibliography
https://arxiv.org/abs/2102.04159: “Deep Residual Learning in Spiking Neural Networks”, Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian

link-bibliography
https://arxiv.org/abs/2102.02888#microsoft: “1-Bit Adam: Communication Efficient Large-Scale Training With Adam’s Convergence Speed”, Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He

link-bibliography
https://arxiv.org/abs/2101.03961#google: “Switch Transformers: Scaling to Trillion Parameter Models With Simple and Efficient Sparsity”, William Fedus, Barret Zoph, Noam Shazeer

link-bibliography
https://arxiv.org/abs/2004.07320#facebook: “Training With Quantization Noise for Extreme Model Compression”, Angela Fan, Pierre Stock, Benjamin Graham, Edouard Grave, Remi Gribonval, Herve Jegou, Arm Holdings, Joulin

link-bibliography
https://arxiv.org/abs/2001.01969: “SWAT: Sparse Weight Activation Training”, Md Aamir Raihan, Tor M. Aamodt

link-bibliography
https://arxiv.org/abs/1910.01055#google: “QUARL: Quantized Reinforcement Learning (ActorQ)”, Maximilian Lam, Sharad Chitlangia, Srivatsan Krishnan, Zishen Wan, Gabriel Barth-Maron, Aleksandra Faust, Vijay Janapa Reddi

link-bibliography
https://www.fast.ai/2018/04/30/dawnbench-fastai/: “Training Imagenet in 3 Hours for $25; and CIFAR-10 for $0.26”, Jeremy Howard

link-bibliography
https://arxiv.org/abs/1802.08530: “Training Wide Residual Networks for Deployment Using a Single Bit for Each Weight”, Mark D. McDonnell

link-bibliography
https://arxiv.org/abs/1712.01887: “Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training”, Yujun Lin, Song Han, Huizi Mao, Yu Wang, William J. Dally

link-bibliography
https://arxiv.org/abs/1711.08141: “Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions”, Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer

link-bibliography
https://arxiv.org/abs/1603.05279: “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks”, Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi

link-bibliography

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