Sasikanth Avancha

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 36

Top Topics

Scalable Distributed

Restricted Boltzmann Machine

Top Venues

ACM Trans. Embed. Comput. Syst.

Publications

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, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR (2024)
Md. Vasimuddin, Ramanarayan Mohanty, Sanchit Misra, Sasikanth Avancha
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling. CoRR (2022)
Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads. Frontiers Appl. Math. Stat. 8 (2022)
Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj D. Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha
DistGNN: scalable distributed training for large-scale graph neural networks. SC (2021)
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Bharat Kaul, Gagandeep Goyal, Ramakrishna Upadrasta
PolyDL: Polyhedral Optimizations for Creation of High-performance DL Primitives. ACM Trans. Archit. Code Optim. 18 (1) (2021)
Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj D. Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks. CoRR (2021)
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. CoRR (2021)
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha, Bharat Kaul, Anand Raghunathan
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks. DAC (2021)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
SEERL: Sample Efficient Ensemble Reinforcement Learning. AAMAS (2021)
Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Narendra Chaudhary, Abhisek Kundu, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads. CoRR (2021)
Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads. SC (2021)
Shail Dave, Riyadh Baghdadi, Tony Nowatzki, Sasikanth Avancha, Aviral Shrivastava, Baoxin Li
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights. Proc. IEEE 109 (10) (2021)
Sanket Tavarageri, Gagandeep Goyal, Sasikanth Avancha, Bharat Kaul, Ramakrishna Upadrasta
AI Powered Compiler Techniques for DL Code Optimization. CoRR (2021)
Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty
Deep Graph Library Optimizations for Intel(R) x86 Architecture. CoRR (2020)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
SEERL: Sample Efficient Ensemble Reinforcement Learning. CoRR (2020)
Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee
dMazeRunner: Optimizing Convolutions on Dataflow Accelerators. ICASSP (2020)
Shail Dave, Riyadh Baghdadi, Tony Nowatzki, Sasikanth Avancha, Aviral Shrivastava, Baoxin Li
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights. CoRR (2020)
Brian Hickmann, Jieasheng Chen, Michael Rotzin, Andrew Yang, Maciej Urbanski, Sasikanth Avancha
Intel Nervana Neural Network Processor-T (NNP-T) Fused Floating Point Many-Term Dot Product. ARITH (2020)
Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke
Harnessing Deep Learning via a Single Building Block. IPDPS (2020)
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Gagandeep Goyal, Ramakrishna Upadrasta, Bharat Kaul
PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives. CoRR (2020)
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract). AAAI (2020)
Sanket Tavarageri, Alexander Heinecke, Sasikanth Avancha, Gagandeep Goyal, Ramakrishna Upadrasta, Bharat Kaul
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives. CoRR (2020)
Shail Dave, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee, Aviral Shrivastava
dMazeRunner: Executing Perfectly Nested Loops on Dataflow Accelerators. ACM Trans. Embed. Comput. Syst. 18 (5s) (2019)
Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke
High-Performance Deep Learning via a Single Building Block. CoRR (2019)
Sudarshan Srinivasan, Pradeep Janedula, Saurabh Dhoble, Sasikanth Avancha, Dipankar Das, Naveen Mellempudi, Bharat Daga, Martin Langhammer, Gregg Baeckler, Bharat Kaul
High Performance Scalable FPGA Accelerator for Deep Neural Networks. CoRR (2019)
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
A Study of BFLOAT16 for Deep Learning Training. CoRR (2019)
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
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. ICLR (Poster) (2018)
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
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. CoRR (2018)
Dharma Teja Vooturi, Dheevatsa Mudigere, Sasikanth Avancha
Hierarchical Block Sparse Neural Networks. CoRR (2018)
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey
On Scale-out Deep Learning Training for Cloud and HPC. CoRR (2018)
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
RAIL: Risk-Averse Imitation Learning. AAMAS (2018)
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj D. Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures. CoRR (2018)
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj D. Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke
Anatomy of high-performance deep learning convolutions on SIMD architectures. SC (2018)
Anirban Santara, Abhishek Naik, Balaraman Ravindran, Dipankar Das, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul
RAIL: Risk-Averse Imitation Learning. CoRR (2017)
Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, Anand Raghunathan
ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks. ISCA (2017)
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidyanathan, Srinivas Sridharan, Dhiraj D. Kalamkar, Bharat Kaul, Pradeep Dubey
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent. CoRR (2016)
Karthikeyan Vaidyanathan, Sasikanth Avancha, Sunil Sherlekar
Exascale Computing & Beyond: Meeting the Challenges. High Performance Computing Workshop (2) (2012)
Sasikanth Avancha, Amit Baxi, David Kotz
Privacy in mobile technology for personal healthcare. ACM Comput. Surv. 45 (1) (2012)
Sasikanth Avancha
A Framework for Trustworthy Service-Oriented Computing (Short Paper). ICISS (2008)
Sasikanth Avancha, Chintan Patel, Anupam Joshi
Ontology-Driven Adaptive Sensor Networks. MobiQuitous (2004)
Sasikanth Avancha, Peter D'Souza, Filip Perich, Anupam Joshi, Yelena Yesha
P2P M-commerce in pervasive environments. SIGecom Exch. 3 (4) (2003)
Sasikanth Avancha, Jeffrey Undercoffer, Anupam Joshi, John Pinkston
Secure sensor networks for perimeter protection. Comput. Networks 43 (4) (2003)
Lalana Kagal, Vlad Korolev, Sasikanth Avancha, Anupam Joshi, Timothy W. Finin, Yelena Yesha
Centaurus: An Infrastructure for Service Management in Ubiquitous Computing Environments. Wirel. Networks 8 (6) (2002)
Filip Perich, Sasikanth Avancha, Dipanjan Chakraborty, Anupam Joshi, Yelena Yesha
Profile Driven Data Management for Pervasive Environments. DEXA (2002)
Timothy W. Finin, Anupam Joshi, Lalana Kagal, Olga Ratsimore, Sasikanth Avancha, Vlad Korolev, Harry Chen, Filip Perich, R. Scott Cost
Intelligent Agents for Mobile and Embedded Devices. Int. J. Cooperative Inf. Syst. 11 (3) (2002)
Bhagyalaxmi Bethala, Anupam Joshi, Dhananjay S. Phatak, Sasikanth Avancha, Tom Goff
Simulation of a Common Access Point for Bluetooth, 802.11 and Wired LANs. PDPTA (2002)
Sasikanth Avancha, Vlad Korolev, Anupam Joshi, Timothy W. Finin, Yelena Yesha
On experiments with a transport protocol for pervasive computing environments. Comput. Networks 40 (4) (2002)
Sasikanth Avancha, Anupam Joshi, Timothy W. Finin
Enhanced Service Discovery in Bluetooth. Computer 35 (6) (2002)
Sasikanth Avancha, Vladimir Korolev, Anupam Joshi, Timothy W. Finin
Transport protocols in wireless networks. ICCCN (2001)