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NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference

Dec 03, 2021
Joonsang Yu, Junki Park, Seongmin Park, Minsoo Kim, Sihwa Lee, Dong Hyun Lee, Jungwook Choi

* 7 pages, 3 figures 

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Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling

Oct 07, 2021
Kyuhong Shim, Iksoo Choi, Wonyong Sung, Jungwook Choi

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Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead

Jan 04, 2021
Muhammad Shafique, Mahum Naseer, Theocharis Theocharides, Christos Kyrkou, Onur Mutlu, Lois Orosa, Jungwook Choi

* IEEE Design and Test (Volume: 37, Issue: 2, April 2020): 30-57 
* Final version appears in 

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Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks

Sep 30, 2020
Yoonho Boo, Sungho Shin, Jungwook Choi, Wonyong Sung

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Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks

Jan 19, 2019
Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, Kailash Gopalakrishnan

* Published as a conference paper in ICLR 2019 

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Training Deep Neural Networks with 8-bit Floating Point Numbers

Dec 19, 2018
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan

* NeurIPS 2018 (12 pages) 

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Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)

Jul 17, 2018
Jungwook Choi, Pierce I-Jen Chuang, Zhuo Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

* arXiv admin note: substantial text overlap with arXiv:1805.06085 

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PACT: Parameterized Clipping Activation for Quantized Neural Networks

Jul 17, 2018
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

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AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training

Dec 07, 2017
Chia-Yu Chen, Jungwook Choi, Daniel Brand, Ankur Agrawal, Wei Zhang, Kailash Gopalakrishnan

* IBM Research AI, 9 pages, 7 figures, AAAI18 accepted 

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