Quantization-Guided Training for Compact TinyML Models

10 March 2021

Papers citing "Quantization-Guided Training for Compact TinyML Models"

23 / 23 papers shown

Title
Subtensor Quantization for Mobilenets Thu Dinh A. Melnikov Vasilios Daskalopoulos S. Chai MQ 49 4 0 04 Nov 2020
Training with Quantization Noise for Extreme Model Compression Angela Fan Pierre Stock Benjamin Graham Edouard Grave Remi Gribonval Hervé Jégou Armand Joulin MQ 70 245 0 15 Apr 2020
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers Zhuohan Li Eric Wallace Sheng Shen Kevin Lin Kurt Keutzer Dan Klein Joseph E. Gonzalez 91 151 0 26 Feb 2020
Bit Efficient Quantization for Deep Neural Networks Prateeth Nayak David C. Zhang S. Chai MQ 48 43 0 07 Oct 2019
Once-for-All: Train One Network and Specialize it for Efficient Deployment Han Cai Chuang Gan Tianzhe Wang Zhekai Zhang Song Han OOD 98 1,277 0 26 Aug 2019
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models Iulia Turc Ming-Wei Chang Kenton Lee Kristina Toutanova 60 224 0 23 Aug 2019
Visual Wake Words Dataset Aakanksha Chowdhery Pete Warden Jonathon Shlens Andrew G. Howard Rocky Rhodes VLM 58 102 0 12 Jun 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 129 18,058 0 28 May 2019
SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training Ahmed T. Elthakeb Prannoy Pilligundla H. Esmaeilzadeh MQ 52 9 0 04 May 2019
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks Samyak Parajuli Aswin Raghavan S. Chai 44 7 0 12 Nov 2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova VLM SSL SSeg 1.4K 94,511 0 11 Oct 2018
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference J. McKinstry S. K. Esser R. Appuswamy Deepika Bablani John V. Arthur Izzet B. Yildiz D. Modha MQ 50 94 0 11 Sep 2018
Quantizing deep convolutional networks for efficient inference: A whitepaper Raghuraman Krishnamoorthi MQ 121 1,013 0 21 Jun 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 202 3,457 0 09 Mar 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew G. Howard Menglong Zhu A. Zhmoginov Liang-Chieh Chen 169 19,204 0 13 Jan 2018
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Benoit Jacob S. Kligys Bo Chen Menglong Zhu Matthew Tang Andrew G. Howard Hartwig Adam Dmitry Kalenichenko MQ 136 3,111 0 15 Dec 2017
Learning Sparse Neural Networks through $L_0$ Regularization Christos Louizos Max Welling Diederik P. Kingma 343 1,144 0 04 Dec 2017
A systematic study of the class imbalance problem in convolutional neural networks Mateusz Buda A. Maki Maciej A. Mazurowski 189 2,352 0 15 Oct 2017
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang Xinyu Zhou Mengxiao Lin Jian Sun AI4TS 132 6,850 0 04 Jul 2017
Pruning Filters for Efficient ConvNets Hao Li Asim Kadav Igor Durdanovic H. Samet H. Graf 3DPC 186 3,692 0 31 Aug 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 231 8,821 0 01 Oct 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 308 19,580 0 09 Mar 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 417 43,234 0 11 Feb 2015