Deep learning scaling is predictable, empirically J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... arXiv preprint arXiv:1712.00409, 2017 | 617 | 2017 |
Sustainable ai: Environmental implications, challenges and opportunities CJ Wu, R Raghavendra, U Gupta, B Acun, N Ardalani, K Maeng, G Chang, ... Proceedings of Machine Learning and Systems 4, 795-813, 2022 | 269 | 2022 |
Stream-dataflow acceleration T Nowatzki, V Gangadhar, N Ardalani, K Sankaralingam Proceedings of the 44th Annual International Symposium on Computer …, 2017 | 195 | 2017 |
Cross-architecture performance prediction (XAPP) using CPU code to predict GPU performance N Ardalani, C Lestourgeon, K Sankaralingam, X Zhu Proceedings of the 48th International Symposium on Microarchitecture, 725-737, 2015 | 141 | 2015 |
Dataperf: Benchmarks for data-centric ai development M Mazumder, C Banbury, X Yao, B Karlaš, W Gaviria Rojas, S Diamos, ... Advances in Neural Information Processing Systems 36, 2024 | 76 | 2024 |
Beyond human-level accuracy: Computational challenges in deep learning J Hestness, N Ardalani, G Diamos Proceedings of the 24th symposium on principles and practice of parallel …, 2019 | 71 | 2019 |
Hybrid Optimization/Heuristic Instruction Scheduling for Programmable Accelerator Codesign T Nowatzki, N Ardalani, K Sankaralingam, J Weng Proceedings of the 27th International Conference on Parallel Architectures …, 2018 | 52 | 2018 |
Deep learning scaling is predictable J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... Empirically. arXiv 1712, 2, 2017 | 51 | 2017 |
Systems and methods for stream-dataflow acceleration wherein a delay is implemented so as to equalize arrival times of data packets at a destination functional unit K Sankaralingam, A Nowatzki, V Gangadhar, P Shah, N Ardalani US Patent 11,048,661, 2021 | 23 | 2021 |
Stream-dataflow acceleration. In 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) T Nowatzki, V Gangadhar, N Ardalani, K Sankaralingam IEEE Proc. ISCA, Toronto, ON, Canada, 24th-28th Jun, 2017 | 23 | 2017 |
Deep learning scaling is predictable, empirically, arXiv J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... arXiv preprint arXiv:1712.00409, 2017 | 23 | 2017 |
A Static Analysis-based Cross-architecture Performance Prediction Using Machine Learning N Ardalani, U Thakker, A Albarghouthi, K Sankaralingam arXiv preprint arXiv:1906.07840, 2019 | 20* | 2019 |
Time and the Value of Data E Valavi, J Hestness, N Ardalani, M Iansiti arXiv preprint arXiv:2203.09118, 2022 | 18 | 2022 |
Deep Learning Scaling is Predictable, Empirically J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... arXiv preprint arXiv:1712.00409, 2017 | 14 | 2017 |
Understanding scaling laws for recommendation models N Ardalani, CJ Wu, Z Chen, B Bhushanam, A Aziz arXiv preprint arXiv:2208.08489, 2022 | 10 | 2022 |
Mp-rec: Hardware-software co-design to enable multi-path recommendation S Hsia, U Gupta, B Acun, N Ardalani, P Zhong, GY Wei, D Brooks, CJ Wu Proceedings of the 28th ACM International Conference on Architectural …, 2023 | 7 | 2023 |
Decoding data quality via synthetic corruptions: Embedding-guided pruning of code data Y Yang, AK Singh, M Elhoushi, A Mahmoud, K Tirumala, F Gloeckle, ... arXiv preprint arXiv:2312.02418, 2023 | 4 | 2023 |
Empirically Characterizing Overparameterization Impact on Convergence N Ardalani, J Hestness, G Diamos | 4 | 2018 |
Data Acquisition: A New Frontier in Data-centric AI L Chen, B Acun, N Ardalani, Y Sun, F Kang, H Lyu, Y Kwon, R Jia, CJ Wu, ... arXiv preprint arXiv:2311.13712, 2023 | 3 | 2023 |
Sieve: Multimodal dataset pruning using image captioning models A Mahmoud, M Elhoushi, A Abbas, Y Yang, N Ardalani, H Leather, ... arXiv preprint arXiv:2310.02110, 2023 | 3 | 2023 |