| Nitro: A framework for adaptive code variant tuning S Muralidharan, M Shantharam, M Hall, M Garland, B Catanzaro Parallel and Distributed Processing Symposium, 2014 IEEE 28th International …, 2014 | 85 | 2014 |
| Architecture-adaptive code variant tuning S Muralidharan, A Roy, M Hall, M Garland, P Rai ACM SIGARCH Computer Architecture News 44 (2), 325-338, 2016 | 34 | 2016 |
| A programmable approach to neural network compression V Joseph, GL Gopalakrishnan, S Muralidharan, M Garland, A Garg IEEE Micro 40 (5), 17-25, 2020 | 33* | 2020 |
| Towards making autotuning mainstream P Basu, M Hall, M Khan, S Maindola, S Muralidharan, S Ramalingam, ... The International journal of high performance computing applications 27 (4 …, 2013 | 26 | 2013 |
| Highlight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity YN Wu, PA Tsai, S Muralidharan, A Parashar, V Sze, J Emer Proceedings of the 56th Annual IEEE/ACM International Symposium on …, 2023 | 22 | 2023 |
| Going beyond classification accuracy metrics in model compression V Joseph, SA Siddiqui, A Bhaskara, G Gopalakrishnan, S Muralidharan, ... arXiv preprint arXiv:2012.01604, 2020 | 22* | 2020 |
| Compact Language Models via Pruning and Knowledge Distillation S Muralidharan, ST Sreenivas, R Joshi, M Chochowski, M Patwary, ... arXiv preprint arXiv:2407.14679, 2024 | 20 | 2024 |
| Llm pruning and distillation in practice: The minitron approach ST Sreenivas, S Muralidharan, R Joshi, M Chochowski, M Patwary, ... arXiv preprint arXiv:2408.11796, 2024 | 8 | 2024 |
| Flextron: Many-in-One Flexible Large Language Model R Cai, S Muralidharan, G Heinrich, H Yin, Z Wang, J Kautz, P Molchanov arXiv preprint arXiv:2406.10260, 2024 | 7 | 2024 |
| Bayesian optimization of sparsity ratios in model compression S Muralidharan, V Joseph, G Animesh, M Garland US Patent App. 16/785,044, 2021 | 6 | 2021 |
| A collection-oriented programming model for performance portability S Muralidharan, M Garland, B Catanzaro, A Sidelnik, M Hall Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of …, 2015 | 6 | 2015 |
| Uniform Sparsity in Deep Neural Networks S Muralidharan Proceedings of Machine Learning and Systems 5, 2023 | 3 | 2023 |
| Efficient Sparsely Activated Transformers S Latifi, S Muralidharan, M Garland arXiv preprint arXiv:2208.14580, 2022 | 3 | 2022 |
| Designing a tunable nested data-parallel programming system S Muralidharan, M Garland, A Sidelnik, M Hall ACM Transactions on Architecture and Code Optimization (TACO) 13 (4), 1-24, 2016 | 3 | 2016 |
| Maskllm: Learnable semi-structured sparsity for large language models G Fang, H Yin, S Muralidharan, G Heinrich, J Pool, J Kautz, P Molchanov, ... arXiv preprint arXiv:2409.17481, 2024 | 2 | 2024 |
| Understanding the Effect of the Long Tail on Neural Network Compression H Dam, V Joseph, A Bhaskara, G Gopalakrishnan, S Muralidharan, ... arXiv preprint arXiv:2306.06238, 2023 | 2 | 2023 |
| Galaxia: A Semi-decentralized System for Implementing Secure-Group P2P Networks S Muralidharan, S Koroth, N Anto, R Pandarachalil 2009 First International Conference on Networks & Communications, 289-294, 2009 | 2 | 2009 |
| The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks C Shinn, C McCarthy, S Muralidharan, M Osama, JD Owens arXiv preprint arXiv:2310.00496, 2023 | 1 | 2023 |
| Abstractions and Strategies for Adaptive Programming S Muralidharan The University of Utah, 2016 | 1 | 2016 |
| EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation SY Liu, H Yang, CY Wang, NC Fung, H Yin, C Sakr, S Muralidharan, ... arXiv preprint arXiv:2410.21271, 2024 | | 2024 |
Michael GarlandSenior Director of Research, NVIDIAVerified email at nvidia.com
