| From high-level deep neural models to FPGAs H Sharma, J Park, D Mahajan, E Amaro, JK Kim, C Shao, A Mishra, ... 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture …, 2016 | 651 | 2016 |
| Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network H Sharma, J Park, N Suda, L Lai, B Chau, JK Kim, V Chandra, ... 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018 | 633 | 2018 |
| General-purpose code acceleration with limited-precision analog computation R St. Amant, A Yazdanbakhsh, J Park, B Thwaites, H Esmaeilzadeh, ... ACM SIGARCH Computer Architecture News 42 (3), 505-516, 2014 | 245 | 2014 |
| Tabla: A unified template-based framework for accelerating statistical machine learning D Mahajan, J Park, E Amaro, H Sharma, A Yazdanbakhsh, JK Kim, ... 2016 IEEE International Symposium on High Performance Computer Architecture …, 2016 | 219 | 2016 |
| Neural acceleration for GPU throughput processors A Yazdanbakhsh, J Park, H Sharma, P Lotfi-Kamran, H Esmaeilzadeh Proceedings of the 48th international symposium on microarchitecture, 482-493, 2015 | 141 | 2015 |
| A network-centric hardware/algorithm co-design to accelerate distributed training of deep neural networks Y Li, J Park, M Alian, Y Yuan, Z Qu, P Pan, R Wang, A Schwing, ... 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture …, 2018 | 104 | 2018 |
| Axilog: Language support for approximate hardware design A Yazdanbakhsh, D Mahajan, B Thwaites, J Park, A Nagendrakumar, ... 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE), 812-817, 2015 | 95 | 2015 |
| Locality-aware dynamic VM reconfiguration on MapReduce clouds J Park, D Lee, B Kim, J Huh, S Maeng Proceedings of the 21st international symposium on High-Performance Parallel …, 2012 | 94 | 2012 |
| Serving heterogeneous machine learning models on {Multi-GPU} servers with {Spatio-Temporal} sharing S Choi, S Lee, Y Kim, J Park, Y Kwon, J Huh 2022 USENIX Annual Technical Conference (USENIX ATC 22), 199-216, 2022 | 91 | 2022 |
| Dnnweaver: From high-level deep network models to fpga acceleration H Sharma, J Park, E Amaro, B Thwaites, P Kotha, A Gupta, JK Kim, ... the Workshop on Cognitive Architectures, 2016 | 83 | 2016 |
| FlexJava: Language support for safe and modular approximate programming J Park, H Esmaeilzadeh, X Zhang, M Naik, W Harris 10th Joint Meeting of the European Software Engineering Conference and the …, 0 | 82* | |
| Towards statistical guarantees in controlling quality tradeoffs for approximate acceleration D Mahajan, A Yazdanbakhsh, J Park, B Thwaites, H Esmaeilzadeh ACM SIGARCH Computer Architecture News 44 (3), 66-77, 2016 | 70 | 2016 |
| Scale-out acceleration for machine learning J Park, H Sharma, D Mahajan, JK Kim, P Olds, H Esmaeilzadeh Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 64 | 2017 |
| Rollback-free value prediction with approximate loads B Thwaites, G Pekhimenko, H Esmaeilzadeh, A Yazdanbakhsh, O Mutlu, ... Proceedings of the 23rd international conference on Parallel architectures …, 2014 | 61 | 2014 |
| AxGames: Towards crowdsourcing quality target determination in approximate computing J Park, E Amaro, D Mahajan, B Thwaites, H Esmaeilzadeh ACM SIGPLAN Notices 51 (4), 623-636, 2016 | 37 | 2016 |
| SLO-aware inference scheduler for heterogeneous processors in edge platforms W Seo, S Cha, Y Kim, J Huh, J Park ACM Transactions on Architecture and Code Optimization (TACO) 18 (4), 1-26, 2021 | 35 | 2021 |
| Mixed-signal charge-domain acceleration of deep neural networks through interleaved bit-partitioned arithmetic S Ghodrati, H Sharma, S Kinzer, A Yazdanbakhsh, J Park, NS Kim, ... Proceedings of the ACM International Conference on Parallel Architectures …, 2020 | 32 | 2020 |
| Common counters: Compressed encryption counters for secure GPU memory S Na, S Lee, Y Kim, J Park, J Huh 2021 IEEE International Symposium on High-Performance Computer Architecture …, 2021 | 30 | 2021 |
| Multi-model machine learning inference serving with gpu spatial partitioning S Choi, S Lee, Y Kim, J Park, Y Kwon, J Huh arXiv preprint arXiv:2109.01611, 2021 | 24 | 2021 |
| FlexBlock: A flexible DNN training accelerator with multi-mode block floating point support SH Noh, J Koo, S Lee, J Park, J Kung IEEE Transactions on Computers 72 (9), 2522-2535, 2023 | 23 | 2023 |
Hadi EsmaeilzadehAssociate Professor; Computer Science and Engineering; University of California, San DiegoVerified email at eng.ucsd.edu
