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Amir Yazdanbakhsh
Amir Yazdanbakhsh
Research Scientist at Google DeepMind
Подтвержден адрес электронной почты в домене google.com - Главная страница
Название
Процитировано
Процитировано
Год
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
14342024
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
3092017
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
2602014
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
2162016
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
213*2018
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
A Yazdanbakhsh, AT Elthakeb, P Pilligundla, FS Mireshghallah, ...
190*2018
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks
K Seshadri, B Akin, J Laudon, R Narayanaswami, A Yazdanbakhsh
IISWC, 2022
160*2022
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
1422015
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
1272018
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
123*2023
Learning Performance-Improving Code Edits
AG Shypula, A Madaan, Y Zeng, U Alon, JR Gardner, Y Yang, M Hashemi, ...
ICLR (Spotlight), 2024
118*2024
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
1002016
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
BH Ahn, P Pilligundla, A Yazdanbakhsh, H Esmaeilzadeh
arXiv preprint arXiv:2001.08743, 2020
972020
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
972015
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
882018
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
692016
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
622014
FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks
SC Kao, S Subramanian, G Agrawal, A Yazdanbakhsh, T Krishna
arXiv preprint arXiv:2107.06419v7, 2022
612022
Towards the Co-design of Neural Networks and Accelerators
Y Zhou, X Dong, T Meng, M Tan, B Akin, D Peng, A Yazdanbakhsh, ...
Proceedings of Machine Learning and Systems 4, 141-152, 2022
53*2022
Data-Driven Offline Optimization for Architecting Hardware Accelerators
A Kumar, A Yazdanbakhsh, M Hashemi, K Swersky, S Levine
arXiv preprint arXiv:2110.11346, 2021
442021
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