| Self-Refine: Iterative Refinement with Self-Feedback A Madaan, N Tandon, P Gupta, S Hallinan, L Gao, S Wiegreffe, U Alon, ... Advances in Neural Information Processing Systems 36, 2024 | 3158 | 2024 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| AxBench: A Multiplatform Benchmark Suite for Approximate Computing A Yazdanbakhsh, D Mahajan, H Esmaeilzadeh, P Lotfi-Kamran IEEE Design & Test 34 (2), 60-68, 2017 | 325 | 2017 |
| 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 | 274 | 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 | 239 | 2016 |
| SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks V Akhlaghi, A Yazdanbakhsh, K Samadi, RK Gupta, H Esmaeilzadeh 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018 | 233 | 2018 |
| ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks A Yazdanbakhsh, AT Elthakeb, P Pilligundla, FS Mireshghallah, ... | 213* | 2018 |
| An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks K Seshadri, B Akin, J Laudon, R Narayanaswami, A Yazdanbakhsh IISWC, 2022 | 211* | 2022 |
| Learning Performance-Improving Code Edits AG Shypula, A Madaan, Y Zeng, U Alon, JR Gardner, Y Yang, M Hashemi, ... ICLR (Spotlight), 2024 | 200 | 2024 |
| What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study A Madaan, K Hermann, A Yazdanbakhsh Findings of the Association for Computational Linguistics: EMNLP 2023, 1448-1535, 2023 | 188* | 2023 |
| 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 | 148 | 2015 |
| GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks A Yazdanbakhsh, H Falahati, PJ Wolfe, H Esmaeilzadeh, K Samadi 2018 ACM/IEEE 45th annual international symposium on computer architecture …, 2018 | 133 | 2018 |
| Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation BH Ahn, P Pilligundla, A Yazdanbakhsh, H Esmaeilzadeh arXiv preprint arXiv:2001.08743, 2020 | 114 | 2020 |
| RFVP: Rollback-free Value Prediction with Safe-to-Approximate Loads A Yazdanbakhsh, G Pekhimenko, B Thwaites, H Esmaeilzadeh, O Mutlu, ... ACM Transactions on Architecture and Code Optimization (TACO) 12 (4), 1-26, 2016 | 104 | 2016 |
| 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 | 102 | 2015 |
| FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks SC Kao, S Subramanian, G Agrawal, A Yazdanbakhsh, T Krishna arXiv preprint arXiv:2107.06419v7, 2022 | 101 | 2022 |
| FlexiGAN: An end-to-end Solution for FPGA Acceleration of Generative Adversarial Networks A Yazdanbakhsh, M Brzozowski, B Khaleghi, S Ghodrati, K Samadi, ... 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom …, 2018 | 93 | 2018 |
| 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 | 72 | 2016 |
| 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 | 63 | 2014 |
| Accelerating Attention through Gradient-based Learned Runtime Pruning Z Li, S Ghodrati, A Yazdanbakhsh, H Esmaeilzadeh, M Kang Proceedings of the 49th Annual International Symposium on Computer …, 2022 | 59 | 2022 |
Hadi EsmaeilzadehAssociate Professor; Computer Science and Engineering; University of California, San DiegoVerified email at eng.ucsd.edu
