| Deep learning scaling is predictable, empirically J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... arXiv preprint arXiv:1712.00409, 2017 | 770 | 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 | 482 | 2022 |
| Stream-dataflow acceleration T Nowatzki, V Gangadhar, N Ardalani, K Sankaralingam Proceedings of the 44th Annual International Symposium on Computer …, 2017 | 218 | 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 | 152 | 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 | 120 | 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 | 84 | 2019 |
| Deep learning scaling is predictable J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... Empirically. arXiv 1712, 2, 2017 | 59 | 2017 |
| 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 | 56 | 2018 |
| 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 | 29 | 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 | 29 | 2017 |
| Time and the Value of Data E Valavi, J Hestness, N Ardalani, M Iansiti arXiv preprint arXiv:2203.09118, 2022 | 24 | 2022 |
| 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 |
| 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 | 21* | 2019 |
| Deep Learning Scaling is Predictable, Empirically J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... arXiv preprint arXiv:1712.00409, 2017 | 21 | 2017 |
| Understanding scaling laws for recommendation models N Ardalani, CJ Wu, Z Chen, B Bhushanam, A Aziz arXiv preprint arXiv:2208.08489, 2022 | 16 | 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 | 15 | 2023 |
| Sieve: Multimodal dataset pruning using image captioning models A Mahmoud, M Elhoushi, A Abbas, Y Yang, N Ardalani, H Leather, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 11 | 2024 |
| 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 | 10 | 2023 |
| Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference H Huang, N Ardalani, A Sun, L Ke, HHS Lee, A Sridhar, S Bhosale, CJ Wu, ... arXiv preprint arXiv:2303.06182, 2023 | 10 | 2023 |
| DeepFlow: A cross-stack pathfinding framework for distributed ai systems N Ardalani, S Pal, P Gupta ACM Transactions on Design Automation of Electronic Systems 29 (2), 1-20, 2024 | 9 | 2024 |
Joel HestnessPrincipal Research Scientist, Cerebras SystemsVerified email at cs.wisc.edu
