High Performance Non-uniform FFT on Modern X86-based Multi-core Systems. - Dhiraj D. Kalamkar; Joshua D. Trzasko; Srinivas Sridharan 002; Mikhail Smelyanskiy; Daehyun Kim 001; Armando Manduca; Yunhong Shu; Matt A. Bernstein; Bharat Kaul; Pradeep Dubey (IPDPS, 2012)

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 001; Aniruddha G. Shet; Bharat Kaul; Bálint Joó; Pradeep Dubey (IPDPS, 2014)

Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. - Dheevatsa Mudigere; Srinivas Sridharan 002; Anand M. Deshpande; Jongsoo Park; Alexander Heinecke; Mikhail Smelyanskiy; Bharat Kaul; Pradeep Dubey; Dinesh K. Kaushik; David E. Keyes (IPDPS, 2015)

Distributed Deep Learning Using Synchronous Stochastic Gradient Descent. - Dipankar Das 002; Sasikanth Avancha; Dheevatsa Mudigere; Karthikeyan Vaidyanathan; Srinivas Sridharan 002; Dhiraj D. Kalamkar; Bharat Kaul; Pradeep Dubey (CoRR, 2016)

ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks. - Swagath Venkataramani; Ashish Ranjan 001; Subarno Banerjee; Dipankar Das 002; Sasikanth Avancha; Ashok Jagannathan; Ajaya Durg; Dheemanth Nagaraj; Bharat Kaul; Pradeep Dubey; Anand Raghunathan (ISCA, 2017)

Ternary Residual Networks. - Abhisek Kundu; Kunal Banerjee 001; Naveen Mellempudi; Dheevatsa Mudigere; Dipankar Das 002; Bharat Kaul; Pradeep Dubey (CoRR, 2017)

Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point. - Naveen Mellempudi; Abhisek Kundu; Dipankar Das 002; Dheevatsa Mudigere; Bharat Kaul (CoRR, 2017)

Ternary Neural Networks with Fine-Grained Quantization. - Naveen Mellempudi; Abhisek Kundu; Dheevatsa Mudigere; Dipankar Das 002; Bharat Kaul; Pradeep Dubey (CoRR, 2017)

RAIL: Risk-Averse Imitation Learning. - Anirban Santara; Abhishek Naik; Balaraman Ravindran; Dipankar Das 002; Dheevatsa Mudigere; Sasikanth Avancha; Bharat Kaul (AAMAS, 2018)

Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-Out Classifiers. - Apoorv Vyas; Nataraj Jammalamadaka; Xia Zhu; Dipankar Das 002; Bharat Kaul; Theodore L. Willke (ECCV (8), 2018)

Mixed Precision Training of Convolutional Neural Networks using Integer Operations. - Dipankar Das 002; Naveen Mellempudi; Dheevatsa Mudigere; Dhiraj D. Kalamkar; Sasikanth Avancha; Kunal Banerjee 001; Srinivas Sridharan 002; Karthik Vaidyanathan; Bharat Kaul; Evangelos Georganas; Alexander Heinecke; Pradeep Dubey; et al. (ICLR (Poster), 2018)

On Scale-out Deep Learning Training for Cloud and HPC. - Srinivas Sridharan 002; Karthikeyan Vaidyanathan; Dhiraj D. Kalamkar; Dipankar Das 002; Mikhail E. Smorkalov; Mikhail Shiryaev; Dheevatsa Mudigere; Naveen Mellempudi; Sasikanth Avancha; Bharat Kaul; Pradeep Dubey (CoRR, 2018)

X-MANN: A Crossbar based Architecture for Memory Augmented Neural Networks. - Ashish Ranjan 001; Shubham Jain; Jacob R. Stevens; Dipankar Das 002; Bharat Kaul; Anand Raghunathan (DAC, 2019)

Manna: An Accelerator for Memory-Augmented Neural Networks. - Jacob R. Stevens; Ashish Ranjan 001; Dipankar Das 002; Bharat Kaul; Anand Raghunathan (MICRO, 2019)

A Study of BFLOAT16 for Deep Learning Training. - Dhiraj D. Kalamkar; Dheevatsa Mudigere; Naveen Mellempudi; Dipankar Das 002; Kunal Banerjee 001; Sasikanth Avancha; Dharma Teja Vooturi; Nataraj Jammalamadaka; Jianyu Huang; Hector Yuen; Jiyan Yang; Jongsoo Park; et al. (CoRR, 2019)

Mixed Precision Training With 8-bit Floating Point. - Naveen Mellempudi; Sudarshan Srinivasan; Dipankar Das 002; Bharat Kaul (CoRR, 2019)

Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support. - Sanket Tavarageri; Srinivas Sridharan 002; Bharat Kaul (CoRR, 2019)

High Performance Scalable FPGA Accelerator for Deep Neural Networks. - Sudarshan Srinivasan; Pradeep Janedula; Saurabh Dhoble; Sasikanth Avancha; Dipankar Das 002; Naveen Mellempudi; Bharat Daga; Martin Langhammer; Gregg Baeckler; Bharat Kaul (CoRR, 2019)

K-TanH: Hardware Efficient Activations For Deep Learning. - Abhisek Kundu; Sudarshan Srinivasan; Eric C. Qin; Dhiraj D. Kalamkar; Naveen K. Mellempudi; Dipankar Das 002; Kunal Banerjee 001; Bharat Kaul; Pradeep Dubey (CoRR, 2019)

ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). - Rohan Saphal; Balaraman Ravindran; Dheevatsa Mudigere; Sasikanth Avancha; Bharat Kaul (AAAI, 2020)

SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training. - Eric Qin 001; Ananda Samajdar; Hyoukjun Kwon; Vineet Nadella; Sudarshan Srinivasan; Dipankar Das 002; Bharat Kaul; Tushar Krishna (HPCA, 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)

SEERL: Sample Efficient Ensemble Reinforcement Learning. - Rohan Saphal; Balaraman Ravindran; Dheevatsa Mudigere; Sasikanth Avancha; Bharat Kaul (AAMAS, 2021)

GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. - Jacob R. Stevens; Dipankar Das 002; Sasikanth Avancha; Bharat Kaul; Anand Raghunathan (DAC, 2021)

AI Powered Compiler Techniques for DL Code Optimization. - Sanket Tavarageri; Gagandeep Goyal; Sasikanth Avancha; Bharat Kaul; Ramakrishna Upadrasta (CoRR, 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)

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)

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)

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 (IPDPS Workshops, 2022)

AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks. - Abhisek Kundu; Naveen K. Mellempudi; Dharma Teja Vooturi; Bharat Kaul; Pradeep Dubey (CoRR, 2023)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)