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Deep Graph Library Optimizations for Intel(R) x86 Architecture

Jul 13, 2020
Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty


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Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights

Jul 02, 2020
Shail Dave, Riyadh Baghdadi, Tony Nowatzki, Sasikanth Avancha, Aviral Shrivastava, Baoxin Li


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PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives

Jun 02, 2020
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Gagandeep Goyal, Ramakrishna Upadrasta, Bharat Kaul

* arXiv admin note: substantial text overlap with arXiv:2002.02145 

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PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives

Feb 06, 2020
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Gagandeep Goyal, Ramakrishna Upadrasta, Bharat Kaul


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SEERL: Sample Efficient Ensemble Reinforcement Learning

Jan 15, 2020
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul


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High Performance Scalable FPGA Accelerator for Deep Neural Networks

Aug 29, 2019
Sudarshan Srinivasan, Pradeep Janedula, Saurabh Dhoble, Sasikanth Avancha, Dipankar Das, Naveen Mellempudi, Bharat Daga, Martin Langhammer, Gregg Baeckler, Bharat Kaul


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High-Performance Deep Learning via a Single Building Block

Jun 18, 2019
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar, Sasikanth Avancha, Anand Venkat, Michael Anderson, Greg Henry, Hans Pabst, Alexander Heinecke


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A Study of BFLOAT16 for Deep Learning Training

Jun 13, 2019
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey


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Hierarchical Block Sparse Neural Networks

Aug 10, 2018
Dharma Teja Vooturi, Dheevatsa Mudigree, Sasikanth Avancha


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Mixed Precision Training of Convolutional Neural Networks using Integer Operations

Feb 23, 2018
Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesus Corbal, Nikita Shustrov, Roma Dubtsov, Evarist Fomenko, Vadim Pirogov

* Published as a conference paper at ICLR 2018 

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On Scale-out Deep Learning Training for Cloud and HPC

Jan 24, 2018
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey

* Accepted in SysML 2018 conference 

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RAIL: Risk-Averse Imitation Learning

Nov 29, 2017
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul

* Accepted for presentation in Deep Reinforcement Learning Symposium at NIPS 2017 

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Distributed Deep Learning Using Synchronous Stochastic Gradient Descent

Feb 22, 2016
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidynathan, Srinivas Sridharan, Dhiraj Kalamkar, Bharat Kaul, Pradeep Dubey


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