World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
10395
World Ranking
9993
National Ranking
4210

Overview

Hadi Esmaeilzadeh is affiliated with the University of California, San Diego in the United States. Their research primarily focuses on computer science, with a significant output in subfields such as artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, hardware and architecture, and computer networks and communications.

They have contributed extensively to several topics within their fields, including advanced neural network applications, parallel computing and optimization techniques, advanced memory and neural computing, ferroelectric and negative capacitance devices, adversarial robustness in machine learning, domain adaptation and few-shot learning, and machine learning and data classification.

Among their recent publications are:

  • Conscious Empathic AI in Service, 2022, Journal of Service Research
  • FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms, 2021, IEEE Transactions on Systems Man and Cybernetics Systems
  • Privacy in Deep Learning: A Survey, 2020, arXiv (Cornell University)
  • ReLeQ: A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks, 2020, IEEE Micro
  • Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation, 2020, arXiv (Cornell University)

Frequent coauthors collaborating with Esmaeilzadeh include:

  • Soroush Ghodrati
  • Sean Kinzer
  • Byung Hoon Ahn
  • Rohan Mahapatra
  • Prannoy Pilligundla

Esmaeilzadeh has regularly published in venues such as arXiv (Cornell University), IEEE Micro, ACM Transactions on Design Automation of Electronic Systems, Journal of Service Research, and IEEE Transactions on Systems Man and Cybernetics Systems.

Best Publications

  • Dark silicon and the end of multicore scaling

    Hadi Esmaeilzadeh;Emily Blem;Renee St. Amant;Karthikeyan Sankaralingam

  • A reconfigurable fabric for accelerating large-scale datacenter services

    Andrew Putnam;Adrian M. Caulfield;Eric S. Chung;Derek Chiou

  • Neural acceleration for general-purpose approximate programs

    Hadi Esmaeilzadeh;Adrian Sampson;Luis Ceze;Doug Burger

  • Architecture support for disciplined approximate programming

    Hadi Esmaeilzadeh;Adrian Sampson;Luis Ceze;Doug Burger

  • Bit fusion: bit-level dynamically composable architecture for accelerating deep neural networks

    Hardik Sharma;Jongse Park;Naveen Suda;Liangzhen Lai

  • From high-level deep neural models to FPGAs

    Hardik Sharma;Jongse Park;Divya Mahajan;Emmanuel Amaro

  • Dark Silicon and the End of Multicore Scaling

    H. Esmaeilzadeh;E. Blem;R. St. Amant;K. Sankaralingam

  • AxBench: A Multiplatform Benchmark Suite for Approximate Computing

    Amir Yazdanbakhsh;Divya Mahajan;Hadi Esmaeilzadeh;Pejman Lotfi-Kamran

  • General-purpose code acceleration with limited-precision analog computation

    Renée St. Amant;Amir Yazdanbakhsh;Jongse Park;Bradley Thwaites

  • Power challenges may end the multicore era

    Hadi Esmaeilzadeh;Emily Blem;Renée St. Amant;Karthikeyan Sankaralingam

  • A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services

    Andrew Putnam;Adrian M. Caulfield;Eric S. Chung;Derek Chiou

  • TABLA: A unified template-based framework for accelerating statistical machine learning

    Divya Mahajan;Jongse Park;Emmanuel Amaro;Hardik Sharma

  • SNNAP: Approximate computing on programmable SoCs via neural acceleration

    Thierry Moreau;Mark Wyse;Jacob Nelson;Adrian Sampson

  • SnaPEA: predictive early activation for reducing computation in deep convolutional neural networks

    Vahideh Akhlaghi;Amir Yazdanbakhsh;Kambiz Samadi;Rajesh K. Gupta

  • Neural acceleration for GPU throughput processors

    Amir Yazdanbakhsh;Jongse Park;Hardik Sharma;Pejman Lotfi-Kamran

  • Looking back on the language and hardware revolutions: measured power, performance, and scaling

    Hadi Esmaeilzadeh;Ting Cao;Yang Xi;Stephen M. Blackburn

  • Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks

    Soroush Ghodrati;Byung Hoon Ahn;Joon Kyung Kim;Sean Kinzer

  • Privacy in Deep Learning: A Survey

    Fatemehsadat Mireshghallah;Mohammadkazem Taram;Praneeth Vepakomma;Abhishek Singh

  • GANAX: a unified MIMD-SIMD acceleration for generative adversarial networks

    Amir Yazdanbakhsh;Kambiz Samadi;Nam Sung Kim;Hadi Esmaeilzadeh

  • A network-centric hardware/algorithm co-design to accelerate distributed training of deep neural networks

    Youjie Li;Jongse Park;Mohammad Alian;Yifan Yuan

  • RFVP: Rollback-Free Value Prediction with Safe-to-Approximate Loads

    Amir Yazdanbakhsh;Gennady Pekhimenko;Bradley Thwaites;Hadi Esmaeilzadeh

Frequent Co-Authors

Doug Burger
Doug Burger Microsoft (United States)
Nam Sung Kim
Nam Sung Kim University of Illinois at Urbana-Champaign
Dean M. Tullsen
Dean M. Tullsen University of California, San Diego
Karthikeyan Sankaralingam
Karthikeyan Sankaralingam University of Wisconsin–Madison
Luis Ceze
Luis Ceze University of Washington
Abbas Rahimi
Abbas Rahimi IBM (United States)
Gennady Pekhimenko
Gennady Pekhimenko University of Toronto
Onur Mutlu
Onur Mutlu ETH Zurich
Todd C. Mowry
Todd C. Mowry Carnegie Mellon University
Scott Hauck
Scott Hauck University of Washington

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