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Amir Yazdanbakhsh
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2020 – today
- 2024
- [j13]Hyungyo Kim
, Gaohan Ye
Exploiting Intel Advanced Matrix Extensions (AMX) for Large Language Model Inference. IEEE Comput. Archit. Lett. 23(1): 117-120 (2024) - [j12]Santosh Pandey
TAO: Re-Thinking DL-based Microarchitecture Simulation. Proc. ACM Meas. Anal. Comput. Syst. 8(2): 28:1-28:25 (2024) - [c38]Rohan Mahapatra
In-Storage Domain-Specific Acceleration for Serverless Computing. ASPLOS (2) 2024: 530-548 - [c37]Soroush Ghodrati
Tandem Processor: Grappling with Emerging Operators in Neural Networks. ASPLOS (2) 2024: 1165-1182 - [c36]Shaojin Ding, David Qiu, David Rim, Yanzhang He, Oleg Rybakov, Bo Li, Rohit Prabhavalkar, Weiran Wang, Tara N. Sainath, Zhonglin Han, Jian Li, Amir Yazdanbakhsh, Shivani Agrawal:
USM-Lite: Quantization and Sparsity Aware Fine-Tuning for Speech Recognition with Universal Speech Models. ICASSP 2024: 10756-10760 - [c35]Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh:
Learning Performance-Improving Code Edits. ICLR 2024 - [c34]Haoran You, Yichao Fu, Zheng Wang, Amir Yazdanbakhsh, Yingyan Celine Lin:
When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models. ICML 2024 - [c33]Yoonsung Kim, Changhun Oh, Jinwoo Hwang, Wonung Kim, Seongryong Oh, Yubin Lee, Hardik Sharma, Amir Yazdanbakhsh, Jongse Park:
DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics. ISCA 2024: 1246-1261 - [c32]Santosh Pandey
TAO: Re-Thinking DL-based Microarchitecture Simulation. SIGMETRICS/Performance (Abstracts) 2024: 23-24 - [i27]Abhimanyu Rajeshkumar Bambhaniya, Amir Yazdanbakhsh, Suvinay Subramanian, Sheng-Chun Kao, Shivani Agrawal, Utku Evci, Tushar Krishna:
Progressive Gradient Flow for Robust N: M Sparsity Training in Transformers. CoRR abs/2402.04744 (2024) - [i26]Yoonsung Kim, Changhun Oh, Jinwoo Hwang, Wonung Kim, Seongryong Oh, Yubin Lee, Hardik Sharma, Amir Yazdanbakhsh, Jongse Park:
DaCapo: Accelerating Continuous Learning in Autonomous Systems for Video Analytics. CoRR abs/2403.14353 (2024) - [i25]Santosh Pandey, Amir Yazdanbakhsh, Hang Liu:
Tao: Re-Thinking DL-based Microarchitecture Simulation. CoRR abs/2404.10921 (2024) - [i24]Mohammad Mozaffari, Amir Yazdanbakhsh, Zhao Zhang, Maryam Mehri Dehnavi:
SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs. CoRR abs/2405.16325 (2024) - [i23]Simla Burcu Harma, Ayan Chakraborty, Elizaveta Kostenok, Danila Mishin, Dongho Ha, Babak Falsafi, Martin Jaggi, Ming Liu, Yunho Oh, Suvinay Subramanian, Amir Yazdanbakhsh:
Effective Interplay between Sparsity and Quantization: From Theory to Practice. CoRR abs/2405.20935 (2024) - [i22]Haoran You, Yipin Guo, Yichao Fu, Wei Zhou, Huihong Shi, Xiaofan Zhang, Souvik Kundu, Amir Yazdanbakhsh, Yingyan Lin:
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization. CoRR abs/2406.05981 (2024) - [i21]Haoran You, Yichao Fu, Zheng Wang, Amir Yazdanbakhsh, Yingyan Lin:
When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models. CoRR abs/2406.07368 (2024) - 2023
- [c31]Sheng-Chun Kao, Suvinay Subramanian, Gaurav Agrawal, Amir Yazdanbakhsh, Tushar Krishna:
FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks. ASPLOS (2) 2023: 295-310 - [c30]Vijay Janapa Reddi, Amir Yazdanbakhsh:
Architecture 2.0: Challenges and Opportunities. DAC 2023: 1-2 - [c29]Aman Madaan, Katherine Hermann, Amir Yazdanbakhsh:
What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study. EMNLP (Findings) 2023: 1448-1535 - [c28]Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh:
STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. ICML 2023: 22812-22824 - [c27]Srivatsan Krishnan
ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design. ISCA 2023: 14:1-14:16 - [c26]Dong Kai Wang
MESA: Microarchitecture Extensions for Spatial Architecture Generation. ISCA 2023: 49:1-49:14 - [c25]Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, Peter Clark:
Self-Refine: Iterative Refinement with Self-Feedback. NeurIPS 2023 - [i20]Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh:
STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. CoRR abs/2302.01172 (2023) - [i19]Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, Amir Yazdanbakhsh:
Learning Performance-Improving Code Edits. CoRR abs/2302.07867 (2023) - [i18]Rohan Mahapatra, Soroush Ghodrati, Byung Hoon Ahn, Sean Kinzer, Shu-Ting Wang, Hanyang Xu, Lavanya Karthikeyan, Hardik Sharma, Amir Yazdanbakhsh, Mohammad Alian, Hadi Esmaeilzadeh:
Domain-Specific Computational Storage for Serverless Computing. CoRR abs/2303.03483 (2023) - [i17]Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, Peter Clark:
Self-Refine: Iterative Refinement with Self-Feedback. CoRR abs/2303.17651 (2023) - [i16]Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan S. Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci:
JaxPruner: A concise library for sparsity research. CoRR abs/2304.14082 (2023) - [i15]Srivatsan Krishnan, Amir Yazdanbakhsh, Shvetank Prakash, Jason Jabbour, Ikechukwu Uchendu, Susobhan Ghosh, Behzad Boroujerdian, Daniel Richins, Devashree Tripathy, Aleksandra Faust, Vijay Janapa Reddi:
ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design. CoRR abs/2306.08888 (2023) - [i14]Shaojin Ding, David Qiu, David Rim, Yanzhang He, Oleg Rybakov, Bo Li, Rohit Prabhavalkar, Weiran Wang, Tara N. Sainath, Shivani Agrawal, Zhonglin Han, Jian Li, Amir Yazdanbakhsh:
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models. CoRR abs/2312.08553 (2023) - 2022
- [c24]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR 2022 - [c23]Ondrej Sýkora, Phitchaya Mangpo Phothilimthana, Charith Mendis
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation. IISWC 2022: 14-26 - [c22]Kiran Seshadri, Berkin Akin, James Laudon, Ravi Narayanaswami, Amir Yazdanbakhsh:
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks. IISWC 2022: 79-91 - [c21]Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Mingu Kang:
Accelerating attention through gradient-based learned runtime pruning. ISCA 2022: 902-915 - [c20]Amir Yazdanbakhsh, Ashkan Moradifirouzabadi
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation. MICRO 2022: 744-762 - [c19]Yanqi Zhou, Xuanyi Dong, Tianjian Meng, Mingxing Tan, Berkin Akin, Daiyi Peng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon:
Towards the Co-design of Neural Networks and Accelerators. MLSys 2022 - [i13]Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Mingu Kang:
Accelerating Attention through Gradient-Based Learned Runtime Pruning. CoRR abs/2204.03227 (2022) - [i12]Amir Yazdanbakhsh, Ashkan Moradifirouzabadi, Zheng Li, Mingu Kang:
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation. CoRR abs/2209.00606 (2022) - [i11]Sheng-Chun Kao, Amir Yazdanbakhsh, Suvinay Subramanian, Shivani Agrawal, Utku Evci, Tushar Krishna:
Training Recipe for N: M Structured Sparsity with Decaying Pruning Mask. CoRR abs/2209.07617 (2022) - [i10]Aman Madaan, Amir Yazdanbakhsh:
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango. CoRR abs/2209.07686 (2022) - [i9]Ondrej Sýkora, Phitchaya Mangpo Phothilimthana, Charith Mendis, Amir Yazdanbakhsh:
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation. CoRR abs/2210.03894 (2022) - 2021
- [i8]Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon:
Apollo: Transferable Architecture Exploration. CoRR abs/2102.01723 (2021) - [i7]Yanqi Zhou, Xuanyi Dong, Berkin Akin, Mingxing Tan, Daiyi Peng, Tianjian Meng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon:
Rethinking Co-design of Neural Architectures and Hardware Accelerators. CoRR abs/2102.08619 (2021) - [i6]Amir Yazdanbakhsh, Kiran Seshadri, Berkin Akin, James Laudon, Ravi Narayanaswami:
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks. CoRR abs/2102.10423 (2021) - [i5]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR abs/2110.11346 (2021) - 2020
- [j11]Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Amir Yazdanbakhsh
ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks. IEEE Micro 40(5): 37-45 (2020) - [c18]Soroush Ghodrati, Hardik Sharma, Sean Kinzer, Amir Yazdanbakhsh
Mixed-Signal Charge-Domain Acceleration of Deep Neural Networks through Interleaved Bit-Partitioned Arithmetic. PACT 2020: 399-411 - [c17]Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh:
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. ICLR 2020 - [i4]Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh:
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. CoRR abs/2001.08743 (2020)
2010 – 2019
- 2019
- [c16]Zhenhong Liu, Amir Yazdanbakhsh
AxMemo: hardware-compiler co-design for approximate code memoization. ISCA 2019: 685-697 - [p1]Amir Yazdanbakhsh, Gennady Pekhimenko, Hadi Esmaeilzadeh, Onur Mutlu, Todd C. Mowry:
Towards Breaking the Memory Bandwidth Wall Using Approximate Value Prediction. Approximate Circuits 2019: 417-441 - [i3]Soroush Ghodrati, Hardik Sharma, Sean Kinzer, Amir Yazdanbakhsh, Kambiz Samadi, Nam Sung Kim, Doug Burger, Hadi Esmaeilzadeh:
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic. CoRR abs/1906.11915 (2019) - 2018
- [b1]Amir Yazdanbakhsh:
Neuro-general computing an acceleration-approximation approach. Georgia Institute of Technology, Atlanta, GA, USA, 2018 - [j10]Zhenhong Liu, Amir Yazdanbakhsh
SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning. IEEE Micro 38(4): 50-59 (2018) - [c15]Amir Yazdanbakhsh
In-DRAM near-data approximate acceleration for GPUs. PACT 2018: 34:1-34:14 - [c14]Amir Yazdanbakhsh
FlexiGAN: An End-to-End Solution for FPGA Acceleration of Generative Adversarial Networks. FCCM 2018: 65-72 - [c13]Amir Yazdanbakhsh
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. ISCA 2018: 650-661 - [c12]Vahideh Akhlaghi, Amir Yazdanbakhsh
SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks. ISCA 2018: 662-673 - [i2]Amir Yazdanbakhsh, Hajar Falahati, Philip J. Wolfe, Kambiz Samadi, Nam Sung Kim, Hadi Esmaeilzadeh:
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. CoRR abs/1806.01107 (2018) - [i1]Ahmed T. Elthakeb, Prannoy Pilligundla, Amir Yazdanbakhsh, Sean Kinzer, Hadi Esmaeilzadeh:
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks. CoRR abs/1811.01704 (2018) - 2017
- [j9]Amir Yazdanbakhsh
AxBench: A Multiplatform Benchmark Suite for Approximate Computing. IEEE Des. Test 34(2): 60-68 (2017) - 2016
- [j8]Amir Yazdanbakhsh
Mitigating the Memory Bottleneck With Approximate Load Value Prediction. IEEE Des. Test 33(1): 32-42 (2016) - [j7]Amir Yazdanbakhsh
RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads. ACM Trans. Archit. Code Optim. 12(4): 62:1-62:26 (2016) - [c11]Atieh Lotfi, Abbas Rahimi, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Rajesh K. Gupta:
Grater: An approximation workflow for exploiting data-level parallelism in FPGA acceleration. DATE 2016: 1279-1284 - [c10]Divya Mahajan
TABLA: A unified template-based framework for accelerating statistical machine learning. HPCA 2016: 14-26 - [c9]Divya Mahajan
Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration. ISCA 2016: 66-77 - 2015
- [j6]Divya Mahajan
Axilog: Abstractions for Approximate Hardware Design and Reuse. IEEE Micro 35(5): 16-30 (2015) - [j5]Amir Yazdanbakhsh
Comprehensive Circuit Failure Prediction for Logic and SRAM Using Virtual Aging. IEEE Micro 35(6): 24-36 (2015) - [c8]Amir Yazdanbakhsh, Divya Mahajan, Bradley Thwaites, Jongse Park, Anandhavel Nagendrakumar, Sindhuja Sethuraman, Kartik Ramkrishnan, Nishanthi Ravindran, Rudra Jariwala, Abbas Rahimi, Hadi Esmaeilzadeh, Kia Bazargan:
Axilog: language support for approximate hardware design. DATE 2015: 812-817 - [c7]Amir Yazdanbakhsh
Online and Operand-Aware Detection of Failures Utilizing False Alarm Vectors. ACM Great Lakes Symposium on VLSI 2015: 149-154 - [c6]Amir Yazdanbakhsh
Neural acceleration for GPU throughput processors. MICRO 2015: 482-493 - 2014
- [j4]Amir Yazdanbakhsh
Implementation-aware selection of the custom instruction set for extensible processors. Microprocess. Microsystems 38(7): 681-691 (2014) - [j3]Amir Yazdanbakhsh
Customized pipeline and instruction set architecture for embedded processing engines. J. Supercomput. 68(2): 948-977 (2014) - [c5]Bradley Thwaites, Gennady Pekhimenko, Hadi Esmaeilzadeh, Amir Yazdanbakhsh
Rollback-free value prediction with approximate loads. PACT 2014: 493-494 - [c4]Renée St. Amant, Amir Yazdanbakhsh
General-purpose code acceleration with limited-precision analog computation. ISCA 2014: 505-516 - 2013
- [j2]Mehdi Kamal, Amir Yazdanbakhsh
A new merit function for custom instruction selection under an area budget constraint. Des. Autom. Embed. Syst. 17(1): 1-25 (2013) - 2012
- [j1]Mostafa E. Salehi
Instruction set architectural guidelines for embedded packet-processing engines. J. Syst. Archit. 58(3-4): 112-125 (2012) - 2011
- [c3]Farshad Firouzi, Amir Yazdanbakhsh
Dynamic Soft Error Hardening via Joint Body Biasing and Dynamic Voltage Scaling. DSD 2011: 385-392 - 2010
- [c2]Ali Azarpeyvand
Instruction reliability analysis for embedded processors. DDECS 2010: 20-23 - [c1]Amir Yazdanbakhsh
Energy-aware design space exploration of registerfile for extensible processors. ICSAMOS 2010: 273-281
Coauthor Index
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last updated on 2024-10-07 22:06 CEST by the dblp team
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