Bharat Kaul

According to our database, Bharat Kaul authored at least 30 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks.

Abhisek Kundu, Naveen K. Mellempudi, Dharma Teja Vooturi, Bharat Kaul, Pradeep Dubey

CoRR, 2023

2022

Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data.

Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2022

2021

PolyDL: Polyhedral Optimizations for Creation of High-performance DL Primitives.

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

ACM Trans. Archit. Code Optim., 2021

MADRaS : Multi Agent Driving Simulator.

Anirban Santara, Sohan Rudra, Sree Aditya Buridi, Meha Kaushik, Abhishek Naik, Bharat Kaul, Balaraman Ravindran

J. Artif. Intell. Res., 2021

Efficient and Generic 1D Dilated Convolution Layer for Deep Learning.

Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul

CoRR, 2021

AI Powered Compiler Techniques for DL Code Optimization.

Sanket Tavarageri, Gagandeep Goyal, Sasikanth Avancha, Bharat Kaul, Ramakrishna Upadrasta

CoRR, 2021

GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks.

Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan

Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021

SEERL: Sample Efficient Ensemble Reinforcement Learning.

Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

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

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

CoRR, 2020

SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training.

Eric Qin, Ananda Samajdar, Hyoukjun Kwon, Vineet Nadella, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul, Tushar Krishna

Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2020

ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract).

Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

K-TanH: Hardware Efficient Activations For Deep Learning.

Abhisek Kundu, Sudarshan Srinivasan, Eric C. Qin, Dhiraj D. Kalamkar, Naveen K. Mellempudi, Dipankar Das, Kunal Banerjee, Bharat Kaul, Pradeep Dubey

CoRR, 2019

High Performance Scalable FPGA Accelerator for Deep Neural Networks.

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

CoRR, 2019

Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support.

Sanket Tavarageri, Srinivas Sridharan, Bharat Kaul

CoRR, 2019

Mixed Precision Training With 8-bit Floating Point.

Naveen Mellempudi, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul

CoRR, 2019

A Study of BFLOAT16 for Deep Learning Training.

Dhiraj D. 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

CoRR, 2019

Manna: An Accelerator for Memory-Augmented Neural Networks.

Jacob R. Stevens, Ashish Ranjan, Dipankar Das, Bharat Kaul, Anand Raghunathan

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019

X-MANN: A Crossbar based Architecture for Memory Augmented Neural Networks.

Ashish Ranjan, Shubham Jain, Jacob R. Stevens, Dipankar Das, Bharat Kaul, Anand Raghunathan

Proceedings of the 56th Annual Design Automation Conference 2019, 2019

2018

On Scale-out Deep Learning Training for Cloud and HPC.

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

CoRR, 2018

Mixed Precision Training of Convolutional Neural Networks using Integer Operations.

Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, Vadim O. Pirogov

Proceedings of the 6th International Conference on Learning Representations, 2018

Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-Out Classifiers.

Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu, Dipankar Das, Bharat Kaul, Theodore L. Willke

Proceedings of the Computer Vision - ECCV 2018, 2018

RAIL: Risk-Averse Imitation Learning.

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

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

2017

Ternary Neural Networks with Fine-Grained Quantization.

Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey

CoRR, 2017

Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point.

Naveen Mellempudi, Abhisek Kundu, Dipankar Das, Dheevatsa Mudigere, Bharat Kaul

CoRR, 2017

Ternary Residual Networks.

Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey

CoRR, 2017

ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks.

Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, Anand Raghunathan

Proceedings of the 44th Annual International Symposium on Computer Architecture, 2017

2016

Distributed Deep Learning Using Synchronous Stochastic Gradient Descent.

Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidyanathan, Srinivas Sridharan, Dhiraj D. Kalamkar, Bharat Kaul, Pradeep Dubey

CoRR, 2016

2015

Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems.

Dheevatsa Mudigere, Srinivas Sridharan, Anand M. Deshpande, Jongsoo Park, Alexander Heinecke, Mikhail Smelyanskiy, Bharat Kaul, Pradeep Dubey, Dinesh K. Kaushik, David E. Keyes

Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium, 2015

2014

Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters.

Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha G. Shet, Bharat Kaul, Bálint Joó, Pradeep Dubey

Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014

2012

High Performance Non-uniform FFT on Modern X86-based Multi-core Systems.

Dhiraj D. Kalamkar, Joshua D. Trzasko, Srinivas Sridharan, Mikhail Smelyanskiy, Daehyun Kim, Armando Manduca, Yunhong Shu, Matt A. Bernstein, Bharat Kaul, Pradeep Dubey

Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium, 2012