Amir Yazdanbakhsh

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 63

Top Venues

Georgia Institute of Technology, Atlanta, GA, USA (PHD)

Education History

Georgia Institute of Technology, Atlanta, GA, USA

PHD

Publications

Mohammad Mozaffari, Amir Yazdanbakhsh, Zhao Zhang, Maryam Mehri Dehnavi
SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs. CoRR (2024)
Hyungyo Kim, Gaohan Ye, Nachuan Wang, Amir Yazdanbakhsh, Nam Sung Kim
Exploiting Intel Advanced Matrix Extensions (AMX) for Large Language Model Inference. IEEE Comput. Archit. Lett. 23 (1) (2024)
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)
Santosh Pandey, Amir Yazdanbakhsh, Hang Liu
TAO: Re-Thinking DL-based Microarchitecture Simulation. SIGMETRICS/Performance (Abstracts) (2024)
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)
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 (2024)
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 (2024)
Santosh Pandey, Amir Yazdanbakhsh, Hang Liu
TAO: Re-Thinking DL-based Microarchitecture Simulation. Proc. ACM Meas. Anal. Comput. Syst. 8 (2) (2024)
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)
Santosh Pandey, Amir Yazdanbakhsh, Hang Liu
Tao: Re-Thinking DL-based Microarchitecture Simulation. CoRR (2024)
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 (2024)
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 (2024)
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 (2024)
Rohan Mahapatra, Soroush Ghodrati, Byung Hoon Ahn, Sean Kinzer, Shu-Ting Wang, Hanyang Xu, Lavanya Karthikeyan, Hardik Sharma, Amir Yazdanbakhsh, Mohammad Alian, Hadi Esmaeilzadeh
In-Storage Domain-Specific Acceleration for Serverless Computing. ASPLOS (2) (2024)
Soroush Ghodrati, Sean Kinzer, Hanyang Xu, Rohan Mahapatra, Yoonsung Kim, Byung Hoon Ahn, Dong Kai Wang, Lavanya Karthikeyan, Amir Yazdanbakhsh, Jongse Park, Nam Sung Kim, Hadi Esmaeilzadeh
Tandem Processor: Grappling with Emerging Operators in Neural Networks. ASPLOS (2) (2024)
Dong Kai Wang, Jiaqi Lou, Naiyin Jin, Edwin Mascarenhas, Rohan Mahapatra, Sean Kinzer, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Nam Sung Kim
MESA: Microarchitecture Extensions for Spatial Architecture Generation. ISCA (2023)
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 (2023)
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)
Vijay Janapa Reddi, Amir Yazdanbakhsh
Architecture 2.0: Challenges and Opportunities. DAC (2023)
Aman Madaan, Katherine Hermann, Amir Yazdanbakhsh
What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study. EMNLP (Findings) (2023)
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)
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 (2023)
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 (2023)
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 (2023)
Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, Amir Yazdanbakhsh
Learning Performance-Improving Code Edits. CoRR (2023)
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 (2023)
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 (2023)
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. ISCA (2023)
Sheng-Chun Kao, Suvinay Subramanian, Gaurav Agrawal, Amir Yazdanbakhsh, Tushar Krishna
FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks. ASPLOS (2) (2023)
Aman Madaan, Amir Yazdanbakhsh
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango. CoRR (2022)
Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Mingu Kang
Accelerating Attention through Gradient-Based Learned Runtime Pruning. CoRR (2022)
Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Mingu Kang
Accelerating attention through gradient-based learned runtime pruning. ISCA (2022)
Ondrej Sýkora, Phitchaya Mangpo Phothilimthana, Charith Mendis, Amir Yazdanbakhsh
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation. CoRR (2022)
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR (2022)
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)
Ondrej Sýkora, Phitchaya Mangpo Phothilimthana, Charith Mendis, Amir Yazdanbakhsh
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation. IISWC (2022)
Amir Yazdanbakhsh, Ashkan Moradifirouzabadi, Zheng Li, Mingu Kang
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation. CoRR (2022)
Amir Yazdanbakhsh, Ashkan Moradifirouzabadi, Zheng Li, Mingu Kang
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation. MICRO (2022)
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 (2022)
Kiran Seshadri, Berkin Akin, James Laudon, Ravi Narayanaswami, Amir Yazdanbakhsh
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks. IISWC (2022)
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 (2021)
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR (2021)
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 (2021)
Amir Yazdanbakhsh, Kiran Seshadri, Berkin Akin, James Laudon, Ravi Narayanaswami
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks. CoRR (2021)
Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. CoRR (2020)
Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Amir Yazdanbakhsh, Hadi Esmaeilzadeh
ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks. IEEE Micro 40 (5) (2020)
Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. ICLR (2020)
Soroush Ghodrati, Hardik Sharma, Sean Kinzer, Amir Yazdanbakhsh, Jongse Park, Nam Sung Kim, Doug Burger, Hadi Esmaeilzadeh
Mixed-Signal Charge-Domain Acceleration of Deep Neural Networks through Interleaved Bit-Partitioned Arithmetic. PACT (2020)
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 (2019)
Zhenhong Liu, Amir Yazdanbakhsh, Dong Kai Wang, Hadi Esmaeilzadeh, Nam Sung Kim
AxMemo: hardware-compiler co-design for approximate code memoization. ISCA (2019)
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 (2018)
Amir Yazdanbakhsh, Choungki Song, Jacob Sacks, Pejman Lotfi-Kamran, Hadi Esmaeilzadeh, Nam Sung Kim
In-DRAM near-data approximate acceleration for GPUs. PACT (2018)
Ahmed T. Elthakeb, Prannoy Pilligundla, Amir Yazdanbakhsh, Sean Kinzer, Hadi Esmaeilzadeh
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks. CoRR (2018)
Amir Yazdanbakhsh, Michael Brzozowski, Behnam Khaleghi, Soroush Ghodrati, Kambiz Samadi, Nam Sung Kim, Hadi Esmaeilzadeh
FlexiGAN: An End-to-End Solution for FPGA Acceleration of Generative Adversarial Networks. FCCM (2018)
Vahideh Akhlaghi, Amir Yazdanbakhsh, Kambiz Samadi, Rajesh K. Gupta, Hadi Esmaeilzadeh
SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks. ISCA (2018)
Amir Yazdanbakhsh, Kambiz Samadi, Nam Sung Kim, Hadi Esmaeilzadeh
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. ISCA (2018)
Zhenhong Liu, Amir Yazdanbakhsh, Taejoon Park, Hadi Esmaeilzadeh, Nam Sung Kim
SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning. IEEE Micro 38 (4) (2018)
Amir Yazdanbakhsh, Divya Mahajan, Hadi Esmaeilzadeh, Pejman Lotfi-Kamran
AxBench: A Multiplatform Benchmark Suite for Approximate Computing. IEEE Des. Test 34 (2) (2017)
Amir Yazdanbakhsh, Gennady Pekhimenko, Bradley Thwaites, Hadi Esmaeilzadeh, Onur Mutlu, Todd C. Mowry
RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads. ACM Trans. Archit. Code Optim. 12 (4) (2016)
Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kyung Kim, Hadi Esmaeilzadeh
TABLA: A unified template-based framework for accelerating statistical machine learning. HPCA (2016)
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)
Amir Yazdanbakhsh, Bradley Thwaites, Hadi Esmaeilzadeh, Gennady Pekhimenko, Onur Mutlu, Todd C. Mowry
Mitigating the Memory Bottleneck With Approximate Load Value Prediction. IEEE Des. Test 33 (1) (2016)
Divya Mahajan, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh
Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration. ISCA (2016)
Amir Yazdanbakhsh, Raghuraman Balasubramanian, Tony Nowatzki, Karthikeyan Sankaralingam
Comprehensive Circuit Failure Prediction for Logic and SRAM Using Virtual Aging. IEEE Micro 35 (6) (2015)
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)
Amir Yazdanbakhsh, David J. Palframan, Azadeh Davoodi, Nam Sung Kim, Mikko H. Lipasti
Online and Operand-Aware Detection of Failures Utilizing False Alarm Vectors. ACM Great Lakes Symposium on VLSI (2015)
Divya Mahajan, Kartik Ramkrishnan, Rudra Jariwala, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Anandhavel Nagendrakumar, Abbas Rahimi, Hadi Esmaeilzadeh, Kia Bazargan
Axilog: Abstractions for Approximate Hardware Design and Reuse. IEEE Micro 35 (5) (2015)
Amir Yazdanbakhsh, Jongse Park, Hardik Sharma, Pejman Lotfi-Kamran, Hadi Esmaeilzadeh
Neural acceleration for GPU throughput processors. MICRO (2015)
Amir Yazdanbakhsh, Mostafa E. Salehi, Sied Mehdi Fakhraie
Customized pipeline and instruction set architecture for embedded processing engines. J. Supercomput. 68 (2) (2014)
Renée St. Amant, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh, Arjang Hassibi, Luis Ceze, Doug Burger
General-purpose code acceleration with limited-precision analog computation. ISCA (2014)
Amir Yazdanbakhsh, Mehdi Kamal, Sied Mehdi Fakhraie, Ali Afzali-Kusha, Saeed Safari, Massoud Pedram
Implementation-aware selection of the custom instruction set for extensible processors. Microprocess. Microsystems 38 (7) (2014)
Bradley Thwaites, Gennady Pekhimenko, Hadi Esmaeilzadeh, Amir Yazdanbakhsh, Onur Mutlu, Jongse Park, Girish Mururu, Todd C. Mowry
Rollback-free value prediction with approximate loads. PACT (2014)
Mehdi Kamal, Amir Yazdanbakhsh, Hamid Noori, Ali Afzali-Kusha, Massoud Pedram
A new merit function for custom instruction selection under an area budget constraint. Des. Autom. Embed. Syst. 17 (1) (2013)
Mostafa E. Salehi, Sied Mehdi Fakhraie, Amir Yazdanbakhsh
Instruction set architectural guidelines for embedded packet-processing engines. J. Syst. Archit. 58 (3-4) (2012)
Farshad Firouzi, Amir Yazdanbakhsh, Hamed Dorosti, Sied Mehdi Fakhraie
Dynamic Soft Error Hardening via Joint Body Biasing and Dynamic Voltage Scaling. DSD (2011)
Amir Yazdanbakhsh, Mehdi Kamal, Mostafa E. Salehi, Hamid Noori, Sied Mehdi Fakhraie
Energy-aware design space exploration of registerfile for extensible processors. ICSAMOS (2010)
Ali Azarpeyvand, Mostafa E. Salehi, Farshad Firouzi, Amir Yazdanbakhsh, Sied Mehdi Fakhraie
Instruction reliability analysis for embedded processors. DDECS (2010)

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Publication Activity (10 Years)

Top Topics

Top Venues

Amir Yazdanbakhsh