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Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization


Jun 16, 2022
Andrea Fasoli, Chia-Yu Chen, Mauricio Serrano, Swagath Venkataramani, George Saon, Xiaodong Cui, Brian Kingsbury, Kailash Gopalakrishnan

* 5 pages, 2 figures, 1 table. Paper accepted to Interspeech 2022 

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4-bit Quantization of LSTM-based Speech Recognition Models


Aug 27, 2021
Andrea Fasoli, Chia-Yu Chen, Mauricio Serrano, Xiao Sun, Naigang Wang, Swagath Venkataramani, George Saon, Xiaodong Cui, Brian Kingsbury, Wei Zhang, Zoltán Tüske, Kailash Gopalakrishnan

* 5 pages, 3 figures, Andrea Fasoli and Chia-Yu Chen equally contributed to this work. Paper accepted to Interspeech 2021 

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ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training


Apr 21, 2021
Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan

* NeurIPS2020 accepted https://proceedings.neurips.cc/paper/2020/hash/9d58963592071dbf38a0fa114269959c-Abstract.html 

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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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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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