Title
Hierarchical Training of Deep Neural Networks Using Early Exiting Yamin Sepehri P. Pad A. C. Yüzügüler P. Frossard L. A. Dunbar 36 9 0 04 Mar 2023
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Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training Charbel Sakr Steve Dai Rangharajan Venkatesan B. Zimmer W. Dally Brucek Khailany MQ 27 41 0 13 Jun 2022
One-way Explainability Isn't The Message A. Srinivasan Michael Bain Enrico W. Coiera 21 2 0 05 May 2022
FlexBlock: A Flexible DNN Training Accelerator with Multi-Mode Block Floating Point Support Seock-Hwan Noh Jahyun Koo Seunghyun Lee Jongse Park Jaeha Kung AI4CE 32 17 0 13 Mar 2022
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks Cheng Gong Ye Lu Kunpeng Xie Zongming Jin Tao Li Yanzhi Wang MQ 27 7 0 08 Sep 2021
A Survey on GAN Acceleration Using Memory Compression Technique Dina Tantawy Mohamed Zahran A. Wassal 42 8 0 14 Aug 2021
Zero-Shot Text-to-Image Generation Aditya A. Ramesh Mikhail Pavlov Gabriel Goh Scott Gray Chelsea Voss Alec Radford Mark Chen Ilya Sutskever VLM 257 4,816 0 24 Feb 2021
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference Steve Dai Rangharajan Venkatesan Haoxing Ren B. Zimmer W. Dally Brucek Khailany MQ 33 68 0 08 Feb 2021
Pruning and Quantization for Deep Neural Network Acceleration: A Survey Tailin Liang C. Glossner Lei Wang Shaobo Shi Xiaotong Zhang MQ 150 678 0 24 Jan 2021
Reducing Inference Latency with Concurrent Architectures for Image Recognition Ramyad Hadidi Jiashen Cao Michael S. Ryoo Hyesoon Kim BDL 14 3 0 13 Nov 2020
FPRaker: A Processing Element For Accelerating Neural Network Training Omar Mohamed Awad Mostafa Mahmoud Isak Edo Vivancos Ali Hadi Zadeh Ciaran Bannon Anand Jayarajan Gennady Pekhimenko Andreas Moshovos 28 15 0 15 Oct 2020
Reducing Data Motion to Accelerate the Training of Deep Neural Networks Sicong Zhuang Cristiano Malossi Marc Casas 27 0 0 05 Apr 2020
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks Léopold Cambier Anahita Bhiwandiwalla Ting Gong M. Nekuii Oguz H. Elibol Hanlin Tang MQ 23 48 0 16 Jan 2020
Towards Unified INT8 Training for Convolutional Neural Network Feng Zhu Ruihao Gong F. Yu Xianglong Liu Yanfei Wang Zhelong Li Xiuqi Yang Junjie Yan MQ 40 151 0 29 Dec 2019
On-Device Machine Learning: An Algorithms and Learning Theory Perspective Sauptik Dhar Junyao Guo Jiayi Liu S. Tripathi Unmesh Kurup Mohak Shah 28 141 0 02 Nov 2019
MLPerf Training Benchmark Arya D. McCarthy Christine Cheng Cody Coleman Greg Diamos Paulius Micikevicius ... Carole-Jean Wu Lingjie Xu Masafumi Yamazaki C. Young Matei A. Zaharia 47 307 0 02 Oct 2019
Automatic Compiler Based FPGA Accelerator for CNN Training S. Venkataramanaiah Yufei Ma Shihui Yin Eriko Nurvitadhi A. Dasu Yu Cao Jae-sun Seo 32 38 0 15 Aug 2019
Deep Learning Training on the Edge with Low-Precision Posits H. F. Langroudi Zachariah Carmichael Dhireesha Kudithipudi MQ 21 14 0 30 Jul 2019
QUOTIENT: Two-Party Secure Neural Network Training and Prediction Nitin Agrawal Ali Shahin Shamsabadi Matt J. Kusner Adria Gascon 30 212 0 08 Jul 2019
Data-Free Quantization Through Weight Equalization and Bias Correction Markus Nagel M. V. Baalen Tijmen Blankevoort Max Welling MQ 19 502 0 11 Jun 2019
Mixed Precision Training With 8-bit Floating Point Naveen Mellempudi Sudarshan Srinivasan Dipankar Das Bharat Kaul MQ 18 69 0 29 May 2019
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report) Zeke Wang Kaan Kara Hantian Zhang Gustavo Alonso O. Mutlu Ce Zhang 31 34 0 08 Mar 2019
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks Charbel Sakr Naigang Wang Chia-Yu Chen Jungwook Choi A. Agrawal Naresh R Shanbhag K. Gopalakrishnan MQ 30 34 0 19 Jan 2019
Collaborative Execution of Deep Neural Networks on Internet of Things Devices Ramyad Hadidi Jiashen Cao Michael S. Ryoo Hyesoon Kim 23 19 0 08 Jan 2019
DSConv: Efficient Convolution Operator Marcelo Gennari Roger Fawcett V. Prisacariu MQ 32 62 0 07 Jan 2019
Rethinking floating point for deep learning Jeff Johnson MQ 19 138 0 01 Nov 2018
Training Deep Neural Network in Limited Precision Hyunsun Park J. Lee Youngmin Oh Sangwon Ha Seungwon Lee 19 9 0 12 Oct 2018
Exploring the Vision Processing Unit as Co-processor for Inference Sergio Rivas-Gomez Antonio J. Peña D. Moloney Erwin Laure Stefano Markidis BDL 19 22 0 09 Oct 2018
Low-Precision Floating-Point Schemes for Neural Network Training Marc Ortiz A. Cristal Eduard Ayguadé Marc Casas MQ 30 22 0 14 Apr 2018
Training DNNs with Hybrid Block Floating Point M. Drumond Tao R. Lin Martin Jaggi Babak Falsafi 25 95 0 04 Apr 2018
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions Stylianos I. Venieris Alexandros Kouris C. Bouganis 19 184 0 15 Mar 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Tal Ben-Nun Torsten Hoefler GNN 33 704 0 26 Feb 2018
A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets Fabian Schuiki Michael Schaffner Frank K. Gürkaynak Luca Benini 31 70 0 19 Feb 2018