Gopalakrishnan, Kailash

8 publications

NeurIPS 2020 FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lin

NeurIPS 2020 ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan

NeurIPS 2020 Ultra-Low Precision 4-Bit Training of Deep Neural Networks Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

ICLR 2019 Accumulation Bit-Width Scaling for Ultra-Low Precision Training of Deep Networks Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, Kailash Gopalakrishnan

NeurIPS 2019 Hybrid 8-Bit Floating Point (HFP8) Training and Inference for Deep Neural Networks Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan

AAAI 2018 AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training Chia-Yu Chen, Jungwook Choi, Daniel Brand, Ankur Agrawal, Wei Zhang, Kailash Gopalakrishnan

NeurIPS 2018 Training Deep Neural Networks with 8-Bit Floating Point Numbers Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan

ICML 2015 Deep Learning with Limited Numerical Precision Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan