Instruction reliability analysis for embedded processors. - Ali Azarpeyvand; Mostafa E. Salehi; Farshad Firouzi; Amir Yazdanbakhsh; Sied Mehdi Fakhraie (DDECS, 2010)

Energy-aware design space exploration of registerfile for extensible processors. - Amir Yazdanbakhsh; Mehdi Kamal; Mostafa E. Salehi; Hamid Noori; Sied Mehdi Fakhraie (ICSAMOS, 2010)

Dynamic Soft Error Hardening via Joint Body Biasing and Dynamic Voltage Scaling. - Farshad Firouzi; Amir Yazdanbakhsh; Hamed Dorosti; Sied Mehdi Fakhraie (DSD, 2011)

Instruction set architectural guidelines for embedded packet-processing engines. - Mostafa E. Salehi; Sied Mehdi Fakhraie; Amir Yazdanbakhsh (J. Syst. Archit., 2012)

A new merit function for custom instruction selection under an area budget constraint. - Mehdi Kamal; Amir Yazdanbakhsh; Hamid Noori; Ali Afzali-Kusha; Massoud Pedram (Des. Autom. Embed. Syst., 2013)

Rollback-free value prediction with approximate loads. - Bradley Thwaites; Gennady Pekhimenko; Hadi Esmaeilzadeh; Amir Yazdanbakhsh; Onur Mutlu; Jongse Park; Girish Mururu; Todd C. Mowry (PACT, 2014)

General-purpose code acceleration with limited-precision analog computation. - Renée St. Amant; Amir Yazdanbakhsh; Jongse Park; Bradley Thwaites; Hadi Esmaeilzadeh; Arjang Hassibi; Luis Ceze; Doug Burger (ISCA, 2014)

Implementation-aware selection of the custom instruction set for extensible processors. - Amir Yazdanbakhsh; Mehdi Kamal; Sied Mehdi Fakhraie; Ali Afzali-Kusha; Saeed Safari; Massoud Pedram (Microprocess. Microsystems, 2014)

Customized pipeline and instruction set architecture for embedded processing engines. - Amir Yazdanbakhsh; Mostafa E. Salehi; Sied Mehdi Fakhraie (J. Supercomput., 2014)

Axilog: language support for approximate hardware design. - Amir Yazdanbakhsh; Divya Mahajan 001; Bradley Thwaites; Jongse Park; Anandhavel Nagendrakumar; Sindhuja Sethuraman; Kartik Ramkrishnan; Nishanthi Ravindran; Rudra Jariwala; Abbas Rahimi; Hadi Esmaeilzadeh; Kia Bazargan (DATE, 2015)

Online and Operand-Aware Detection of Failures Utilizing False Alarm Vectors. - Amir Yazdanbakhsh; David J. Palframan; Azadeh Davoodi; Nam Sung Kim; Mikko H. Lipasti (ACM Great Lakes Symposium on VLSI, 2015)

Neural acceleration for GPU throughput processors. - Amir Yazdanbakhsh; Jongse Park; Hardik Sharma; Pejman Lotfi-Kamran; Hadi Esmaeilzadeh (MICRO, 2015)

Axilog: Abstractions for Approximate Hardware Design and Reuse. - Divya Mahajan 001; Kartik Ramkrishnan; Rudra Jariwala; Amir Yazdanbakhsh; Jongse Park; Bradley Thwaites; Anandhavel Nagendrakumar; Abbas Rahimi; Hadi Esmaeilzadeh; Kia Bazargan (IEEE Micro, 2015)

Comprehensive Circuit Failure Prediction for Logic and SRAM Using Virtual Aging. - Amir Yazdanbakhsh; Raghuraman Balasubramanian; Tony Nowatzki; Karthikeyan Sankaralingam (IEEE Micro, 2015)

Grater: An approximation workflow for exploiting data-level parallelism in FPGA acceleration. - Atieh Lotfi; Abbas Rahimi; Amir Yazdanbakhsh; Hadi Esmaeilzadeh; Rajesh K. Gupta 001 (DATE, 2016)

TABLA: A unified template-based framework for accelerating statistical machine learning. - Divya Mahajan 001; Jongse Park; Emmanuel Amaro; Hardik Sharma; Amir Yazdanbakhsh; Joon Kyung Kim; Hadi Esmaeilzadeh (HPCA, 2016)

Towards Statistical Guarantees in Controlling Quality Tradeoffs for Approximate Acceleration. - Divya Mahajan 001; Amir Yazdanbakhsh; Jongse Park; Bradley Thwaites; Hadi Esmaeilzadeh (ISCA, 2016)

Mitigating the Memory Bottleneck With Approximate Load Value Prediction. - Amir Yazdanbakhsh; Bradley Thwaites; Hadi Esmaeilzadeh; Gennady Pekhimenko; Onur Mutlu; Todd C. Mowry (IEEE Des. Test, 2016)

RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads. - Amir Yazdanbakhsh; Gennady Pekhimenko; Bradley Thwaites; Hadi Esmaeilzadeh; Onur Mutlu; Todd C. Mowry (ACM Trans. Archit. Code Optim., 2016)

AxBench: A Multiplatform Benchmark Suite for Approximate Computing. - Amir Yazdanbakhsh; Divya Mahajan 001; Hadi Esmaeilzadeh; Pejman Lotfi-Kamran (IEEE Des. Test, 2017)

In-DRAM near-data approximate acceleration for GPUs. - Amir Yazdanbakhsh; Choungki Song; Jacob Sacks; Pejman Lotfi-Kamran; Hadi Esmaeilzadeh; Nam Sung Kim (PACT, 2018)

FlexiGAN: An End-to-End Solution for FPGA Acceleration of Generative Adversarial Networks. - Amir Yazdanbakhsh; Michael Brzozowski; Behnam Khaleghi; Soroush Ghodrati; Kambiz Samadi; Nam Sung Kim; Hadi Esmaeilzadeh (FCCM, 2018)

SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks. - Vahideh Akhlaghi; Amir Yazdanbakhsh; Kambiz Samadi; Rajesh K. Gupta 001; Hadi Esmaeilzadeh (ISCA, 2018)

GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. - Amir Yazdanbakhsh; Kambiz Samadi; Nam Sung Kim; Hadi Esmaeilzadeh (ISCA, 2018)

GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks. - Amir Yazdanbakhsh; Hajar Falahati; Philip J. Wolfe; Kambiz Samadi; Nam Sung Kim; Hadi Esmaeilzadeh (CoRR, 2018)

ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks. - Ahmed T. Elthakeb; Prannoy Pilligundla; Amir Yazdanbakhsh; Sean Kinzer; Hadi Esmaeilzadeh (CoRR, 2018)

SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning. - Zhenhong Liu; Amir Yazdanbakhsh; Taejoon Park; Hadi Esmaeilzadeh; Nam Sung Kim (IEEE Micro, 2018)

Neuro-general computing an acceleration-approximation approach. - Amir Yazdanbakhsh (2018)

Towards Breaking the Memory Bandwidth Wall Using Approximate Value Prediction. - Amir Yazdanbakhsh; Gennady Pekhimenko; Hadi Esmaeilzadeh; Onur Mutlu; Todd C. Mowry (Approximate Circuits, 2019)

AxMemo: hardware-compiler co-design for approximate code memoization. - Zhenhong Liu; Amir Yazdanbakhsh; Dong Kai Wang; Hadi Esmaeilzadeh; Nam Sung Kim (ISCA, 2019)

Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic. - Soroush Ghodrati; Hardik Sharma; Sean Kinzer; Amir Yazdanbakhsh; Kambiz Samadi; Nam Sung Kim; Doug Burger; Hadi Esmaeilzadeh (CoRR, 2019)

Mixed-Signal Charge-Domain Acceleration of Deep Neural Networks through Interleaved Bit-Partitioned Arithmetic. - Soroush Ghodrati; Hardik Sharma; Sean Kinzer; Amir Yazdanbakhsh; Jongse Park; Nam Sung Kim; Doug Burger; Hadi Esmaeilzadeh (PACT, 2020)

Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. - Byung Hoon Ahn; Prannoy Pilligundla; Amir Yazdanbakhsh; Hadi Esmaeilzadeh (ICLR, 2020)

ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks. - Ahmed T. Elthakeb; Prannoy Pilligundla; Fatemehsadat Mireshghallah; Amir Yazdanbakhsh; Hadi Esmaeilzadeh (IEEE Micro, 2020)

Apollo: Transferable Architecture Exploration. - Amir Yazdanbakhsh; Christof Angermüller; Berkin Akin; Yanqi Zhou; Albin Jones; Milad Hashemi; Kevin Swersky; Satrajit Chatterjee; Ravi Narayanaswami; James Laudon (CoRR, 2021)

Rethinking Co-design of Neural Architectures and Hardware Accelerators. - Yanqi Zhou; Xuanyi Dong; Berkin Akin; Mingxing Tan; Daiyi Peng; Tianjian Meng; Amir Yazdanbakhsh; Da Huang; Ravi Narayanaswami; James Laudon (CoRR, 2021)

Data-Driven Offline Optimization for Architecting Hardware Accelerators. - Aviral Kumar; Amir Yazdanbakhsh; Milad Hashemi; Kevin Swersky; Sergey Levine (ICLR, 2022)

An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks. - Kiran Seshadri; Berkin Akin; James Laudon; Ravi Narayanaswami; Amir Yazdanbakhsh (IISWC, 2022)

GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation. - Ondrej Sýkora; Phitchaya Mangpo Phothilimthana; Charith Mendis; Amir Yazdanbakhsh (IISWC, 2022)

Accelerating attention through gradient-based learned runtime pruning. - Zheng Li; Soroush Ghodrati; Amir Yazdanbakhsh; Hadi Esmaeilzadeh; Mingu Kang (ISCA, 2022)

Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation. - Amir Yazdanbakhsh; Ashkan Moradifirouzabadi; Zheng Li; Mingu Kang (MICRO, 2022)

Towards the Co-design of Neural Networks and Accelerators. - Yanqi Zhou; Xuanyi Dong; Tianjian Meng; Mingxing Tan; Berkin Akin; Daiyi Peng; Amir Yazdanbakhsh; Da Huang; Ravi Narayanaswami; James Laudon (MLSys, 2022)

Training Recipe for N: M Structured Sparsity with Decaying Pruning Mask. - Sheng-Chun Kao; Amir Yazdanbakhsh; Suvinay Subramanian; Shivani Agrawal; Utku Evci; Tushar Krishna (CoRR, 2022)

Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango. - Aman Madaan; Amir Yazdanbakhsh (CoRR, 2022)

FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks. - Sheng-Chun Kao; Suvinay Subramanian; Gaurav Agrawal; Amir Yazdanbakhsh; Tushar Krishna (ASPLOS (2), 2023)

Architecture 2.0: Challenges and Opportunities. - Vijay Janapa Reddi; Amir Yazdanbakhsh (DAC, 2023)

What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study. - Aman Madaan; Katherine Hermann; Amir Yazdanbakhsh (EMNLP (Findings), 2023)

STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. - Yucheng Lu; Shivani Agrawal; Suvinay Subramanian; Oleg Rybakov; Christopher De Sa; Amir Yazdanbakhsh (ICML, 2023)

ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design. - Srivatsan Krishnan; Amir Yazdanbakhsh; Shvetank Prakash; Jason Jabbour; Ikechukwu Uchendu; Susobhan Ghosh; Behzad Boroujerdian; Daniel Richins; Devashree Tripathy; Aleksandra Faust; Vijay Janapa Reddi (ISCA, 2023)

MESA: Microarchitecture Extensions for Spatial Architecture Generation. - Dong Kai Wang; Jiaqi Lou; Naiyin Jin; Edwin Mascarenhas; Rohan Mahapatra; Sean Kinzer; Soroush Ghodrati; Amir Yazdanbakhsh; Hadi Esmaeilzadeh; Nam Sung Kim (ISCA, 2023)

Self-Refine: Iterative Refinement with Self-Feedback. - Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon 002; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Shashank Gupta; Bodhisattwa Prasad Majumder; et al. (NeurIPS, 2023)

Learning Performance-Improving Code Edits. - Aman Madaan; Alexander Shypula; Uri Alon 002; Milad Hashemi; Parthasarathy Ranganathan; Yiming Yang; Graham Neubig; Amir Yazdanbakhsh (CoRR, 2023)

Domain-Specific Computational Storage for Serverless Computing. - Rohan Mahapatra; Soroush Ghodrati; Byung Hoon Ahn; Sean Kinzer; Shu-Ting Wang; Hanyang Xu 002; Lavanya Karthikeyan; Hardik Sharma; Amir Yazdanbakhsh; Mohammad Alian; Hadi Esmaeilzadeh (CoRR, 2023)

Self-Refine: Iterative Refinement with Self-Feedback. - Aman Madaan; Niket Tandon; Prakhar Gupta; Skyler Hallinan; Luyu Gao; Sarah Wiegreffe; Uri Alon 002; Nouha Dziri; Shrimai Prabhumoye; Yiming Yang; Sean Welleck; Bodhisattwa Prasad Majumder; et al. (CoRR, 2023)

Tandem Processor: Grappling with Emerging Operators in Neural Networks. - Soroush Ghodrati; Sean Kinzer; Hanyang Xu 002; Rohan Mahapatra; Yoonsung Kim; Byung Hoon Ahn; Dong Kai Wang; Lavanya Karthikeyan; Amir Yazdanbakhsh; Jongse Park; Nam Sung Kim; Hadi Esmaeilzadeh (ASPLOS (2), 2024)

In-Storage Domain-Specific Acceleration for Serverless Computing. - Rohan Mahapatra; Soroush Ghodrati; Byung Hoon Ahn; Sean Kinzer; Shu-Ting Wang; Hanyang Xu 002; Lavanya Karthikeyan; Hardik Sharma; Amir Yazdanbakhsh; Mohammad Alian; Hadi Esmaeilzadeh (ASPLOS (2), 2024)

Jaxpruner: A Concise Library for Sparsity Research. - Joo Hyung Lee; Wonpyo Park; Nicole Mitchell; Jonathan Pilault; Johan Samir Obando Ceron; Han-Byul Kim; Namhoon Lee; Elias Frantar; Yun Long; Amir Yazdanbakhsh; Woohyun Han; Shivani Agrawal; et al. (CPAL, 2024)

USM-Lite: Quantization and Sparsity Aware Fine-Tuning for Speech Recognition with Universal Speech Models. - Shaojin Ding; David Qiu; David Rim; Yanzhang He; Oleg Rybakov; Bo Li 028; Rohit Prabhavalkar; Weiran Wang; Tara N. Sainath; Zhonglin Han; Jian Li; Amir Yazdanbakhsh; et al. (ICASSP, 2024)

Learning Performance-Improving Code Edits. - Alexander Shypula; Aman Madaan; Yimeng Zeng; Uri Alon 002; Jacob R. Gardner; Yiming Yang; Milad Hashemi; Graham Neubig; Parthasarathy Ranganathan; Osbert Bastani; Amir Yazdanbakhsh (ICLR, 2024)

When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models. - Haoran You; Yichao Fu; Zheng Wang; Amir Yazdanbakhsh; Yingyan Celine Lin (ICML, 2024)

DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics. - Yoonsung Kim; Changhun Oh; Jinwoo Hwang; Wonung Kim; Seongryong Oh; Yubin Lee 002; Hardik Sharma; Amir Yazdanbakhsh; Jongse Park (ISCA, 2024)

CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming. - Ali TehraniJamsaz; Arijit Bhattacharjee; Le Chen; Nesreen K. Ahmed; Amir Yazdanbakhsh; Ali Jannesari (NeurIPS, 2024)

ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization. - Haoran You; Yipin Guo; Yichao Fu; Wei Zhou; Huihong Shi; Xiaofan Zhang 001; Souvik Kundu 009; Amir Yazdanbakhsh; Yingyan (Celine) Lin (NeurIPS, 2024)

Exploiting Intel Advanced Matrix Extensions (AMX) for Large Language Model Inference. - Hyungyo Kim; Gaohan Ye; Nachuan Wang; Amir Yazdanbakhsh; Nam Sung Kim (IEEE Comput. Archit. Lett., 2024)

Progressive Gradient Flow for Robust N: M Sparsity Training in Transformers. - Abhimanyu Rajeshkumar Bambhaniya; Amir Yazdanbakhsh; Suvinay Subramanian; Sheng-Chun Kao; Shivani Agrawal; Utku Evci; Tushar Krishna (CoRR, 2024)

TAO: Re-Thinking DL-based Microarchitecture Simulation. - Santosh Pandey; Amir Yazdanbakhsh; Hang Liu 001 (Proc. ACM Meas. Anal. Comput. Syst., 2024)

Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers. - Abhimanyu Rajeshkumar Bambhaniya; Amir Yazdanbakhsh; Suvinay Subramanian; Sheng-Chun Kao; Shivani Agrawal; Utku Evci; Tushar Krishna (CPAL, 2025)

Effective Interplay between Sparsity and Quantization: From Theory to Practice. - Simla Burcu Harma; Ayan Chakraborty 005; Elizaveta Kostenok; Danila Mishin; Dongho Ha; Babak Falsafi; Martin Jaggi; Ming Liu; Yunho Oh; Suvinay Subramanian; Amir Yazdanbakhsh (ICLR, 2025)

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws. - Tian Jin; Ahmed Imtiaz Humayun; Utku Evci; Suvinay Subramanian; Amir Yazdanbakhsh; Dan Alistarh; Gintare Karolina Dziugaite (ICLR, 2025)

SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs. - Mohammad Mozaffari; Amir Yazdanbakhsh; Zhao Zhang; Maryam Mehri Dehnavi (ICLR, 2025)

RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving. - Wenqi Jiang 001; Suvinay Subramanian; Cat Graves; Gustavo Alonso; Amir Yazdanbakhsh; Vidushi Dadu (ISCA, 2025)

LIA: A Single-GPU LLM Inference Acceleration with Cooperative AMX-Enabled CPU-GPU Computation and CXL Offloading. - Hyungyo Kim; Nachuan Wang; Qirong Xia; Jinghan Huang 001; Amir Yazdanbakhsh; Nam Sung Kim (ISCA, 2025)

Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion. - Arash Nasr-Esfahany; Mohammad Alizadeh; Victor Lee; Hanna Alam; Brett W. Coon; David E. Culler; Vidushi Dadu; Martin Dixon; Henry M. Levy; Santosh Pandey; Parthasarathy Ranganathan; Amir Yazdanbakhsh (ISCA, 2025)

QuArch: A Question-Answering Dataset for AI Agents in Computer Architecture. - Shvetank Prakash; Andrew Cheng; Jason Yik; Arya Tschand; Radhika Ghosal; Ikechukwu Uchendu; Jessica Quaye; Jeffrey Ma; Shreyas Grampurohit; Sofia Giannuzzi; Arnav Balyan; Fin Amin; et al. (IEEE Comput. Archit. Lett., 2025)

Architecture 2.0: Foundations of Artificial Intelligence Agents for Modern Computer System Design. - Vijay Janapa Reddi; Amir Yazdanbakhsh (Computer, 2025)

Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding. - Tian Jin; Ellie Y. Cheng; Zachary Ankner; Nikunj Saunshi; Blake M. Elias; Amir Yazdanbakhsh; Jonathan Ragan-Kelley; Suvinay Subramanian; Michael Carbin (CoRR, 2025)

ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers. - Hannah Lin; Martin Maas 001; Maximilian Roquemore; Arman Hasanzadeh; Fred Lewis; Yusuf Simonson; Tzu-Wei Yang; Amir Yazdanbakhsh; Deniz Altinbüken; Florin Papa; Maggie Nolan Edmonds; Aditya Patil; et al. (CoRR, 2025)

Beyond Moore's Law: Harnessing the Redshift of Generative AI with Effective Hardware-Software Co-Design. - Amir Yazdanbakhsh (CoRR, 2025)

Understanding and Optimizing Multi-Stage AI Inference Pipelines. - Abhimanyu Rajeshkumar Bambhaniya; Hanjiang Wu; Suvinay Subramanian; Sudarshan Srinivasan; Souvik Kundu 009; Amir Yazdanbakhsh; Midhilesh Elavazhagan; Madhu Kumar; Tushar Krishna (CoRR, 2025)

COSMIC: Enabling Full-Stack Co-Design and Optimization of Distributed Machine Learning Systems. - Aditi Raju; Jared Ni; William Won; Changhai Man; Srivatsan Krishnan; Srinivas Sridharan 002; Amir Yazdanbakhsh; Tushar Krishna; Vijay Janapa Reddi (CoRR, 2025)

SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity. - Samir Khaki; Xiuyu Li; Junxian Guo; Ligeng Zhu; Chenfeng Xu; Konstantinos N. Plataniotis; Amir Yazdanbakhsh; Kurt Keutzer; Song Han 003; Zhijian Liu (CoRR, 2025)