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D-Index & Metrics

Computer Science

D-Index
34
Citations
21731
World Ranking
11862
National Ranking
4836

Overview

Cliff Young is affiliated with Google in the United States and is an active researcher primarily in the field of Computer Science. Their published work spans a variety of topics centered on Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Hardware and Architecture, and Cognitive Neuroscience.

Young's research contributions include a number of papers on advanced neural network applications, stochastic gradient optimization techniques, and parallel computing and optimization techniques. Their work also touches on computational physics, advanced memory systems, neural dynamics, and reservoir computing.

Frequent coauthors collaborating with Young include Norman P. Jouppi, George Thomas Kurian, Nishant Patil, Doe Hyun Yoon, and Sheng Li.

They have published articles in several prominent venues, including:

  • IEEE Micro
  • arXiv (Cornell University)
  • Communications of the ACM
  • Nature
  • Computer

Notable recent papers authored or coauthored by Young include:

  • A domain-specific supercomputer for training deep neural networks, 2020, Communications of the ACM
  • The Design Process for Google's Training Chips: TPUv2 and TPUv3, 2021, IEEE Micro
  • Neuromorphic computing at scale, 2025, Nature
  • MegaBlocks: Efficient Sparse Training with Mixture-of-Experts, 2022, arXiv (Cornell University)
  • TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings, 2023, arXiv (Cornell University)

Their main fields of study include:

  • Computer Science

Subfields of study that Cliff Young specializes in cover:

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Cognitive Neuroscience

The central topics addressed in their research are:

  • Advanced Neural Network Applications
  • Stochastic Gradient Optimization Techniques
  • Parallel Computing and Optimization Techniques
  • Computational Physics and Python Applications
  • Advanced Memory and Neural Computing
  • Neural dynamics and brain function
  • Neural Networks and Reservoir Computing

Best Publications

  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le

  • In-Datacenter Performance Analysis of a Tensor Processing Unit

    Norman P. Jouppi;Cliff Young;Nishant Patil;David Patterson

  • In-Datacenter Performance Analysis of a Tensor Processing Unit

    Norman P. Jouppi;Cliff Young;Nishant Patil;David Patterson

  • Anton, a special-purpose machine for molecular dynamics simulation

    David E. Shaw;Martin M. Deneroff;Ron O. Dror;Jeffrey S. Kuskin

  • Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer

    David E. Shaw;J. P. Grossman;Joseph A. Bank;Brannon Batson

  • Millisecond-scale molecular dynamics simulations on Anton

    David E. Shaw;Ron O. Dror;John K. Salmon;J. P. Grossman

  • Embedded Computing: A VLIW Approach to Architecture, Compilers and Tools

    Joseph A. Fisher;Paolo Faraboschi;Cliff Young

  • TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings

    Unknown

  • Ten Lessons From Three Generations Shaped Google’s TPUv4i : Industrial Product

    Norman P. Jouppi;Doe Hyun Yoon;Matthew Ashcraft;Mark Gottscho

  • Motivation for and Evaluation of the First Tensor Processing Unit

    Norman Jouppi;Cliff Young;Nishant Patil;David Patterson

  • A domain-specific supercomputer for training deep neural networks

    Norman P. Jouppi;Doe Hyun Yoon;George Kurian;Sheng Li

  • Mesh-TensorFlow: Deep Learning for Supercomputers

    Noam Shazeer;Youlong Cheng;Niki J. Parmar;Dustin Tran

  • A comparative analysis of schemes for correlated branch prediction

    Cliff Young;Nicolas Gloy;Michael D. Smith

  • A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution

    Jeff Dean;David Patterson;Cliff Young

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

  • A domain-specific architecture for deep neural networks

    Norman P. Jouppi;Cliff Young;Nishant Patil;David Patterson

  • Improving the accuracy of static branch prediction using branch correlation

    Cliff Young;Michael D. Smith

  • MLPerf Training Benchmark

    Peter Mattson;Christine Cheng;Gregory F. Diamos;Cody Coleman

  • Sparse GPU Kernels for Deep Learning

    Trevor Gale;Matei Zaharia;Cliff Young;Erich Elsen

  • The Design Process for Google's Training Chips: TPUv2 and TPUv3

    Thomas Norrie;Nishant Patil;Doe Hyun Yoon;George Kurian

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

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

  • Instruction scheduling for instruction level parallel processors

    P. Faraboschi;J.A. Fisher;C. Young

Frequent Co-Authors

David E. Shaw
David E. Shaw D. E. Shaw Research
David A. Patterson
David A. Patterson University of California, Berkeley
Norman P. Jouppi
Norman P. Jouppi Google (United States)
Jeffrey Dean
Jeffrey Dean Google (United States)
Matei Zaharia
Matei Zaharia University of California, Berkeley
Hadi Esmaeilzadeh
Hadi Esmaeilzadeh University of California, San Diego
Nam Sung Kim
Nam Sung Kim University of Illinois at Urbana-Champaign
Vijay K. Vasudevan
Vijay K. Vasudevan Google (United States)
Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Dustin Tran
Dustin Tran Google (United States)

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