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pro vyhledávání: '"Vooturi, Dharma Teja"'
Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learnable thresholds to efficiently explore the non-uniform distribution of
Externí odkaz:
http://arxiv.org/abs/2304.06941
Sparse neural networks are shown to give accurate predictions competitive to denser versions, while also minimizing the number of arithmetic operations performed. However current hardware like GPU's can only exploit structured sparsity patterns for b
Externí odkaz:
http://arxiv.org/abs/2006.13486
Autor:
Kalamkar, Dhiraj, Mudigere, Dheevatsa, Mellempudi, Naveen, Das, Dipankar, Banerjee, Kunal, Avancha, Sasikanth, Vooturi, Dharma Teja, Jammalamadaka, Nataraj, Huang, Jianyu, Yuen, Hector, Yang, Jiyan, Park, Jongsoo, Heinecke, Alexander, Georganas, Evangelos, Srinivasan, Sudarshan, Kundu, Abhisek, Smelyanskiy, Misha, Kaul, Bharat, Dubey, Pradeep
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training across image classification, speech recognition, language modeling, generat
Externí odkaz:
http://arxiv.org/abs/1905.12322
Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies are much lower than that of dense DNNs on regular parallel hardware such
Externí odkaz:
http://arxiv.org/abs/1808.03420
Autor:
Vooturi, Dharma Teja, Goyal, Saurabh, Choudhury, Anamitra R., Sabharwal, Yogish, Verma, Ashish
Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on cloud. Prior work has considered reduc
Externí odkaz:
http://arxiv.org/abs/1711.00244
Publikováno v:
Concurrency & Computation: Practice & Experience; 6/25/2023, Vol. 35 Issue 14, p1-14, 14p
Akademický článek
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Publikováno v:
Parallel Processing & Applied Mathematics: 11th International Conference, PPAM 2015, Krakow, Poland, September 6-9, 2015. Revised Selected Papers, Part I; 2016, p106-115, 10p