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Computer Science

D-Index
66
Citations
22770
World Ranking
2278
National Ranking
1139

Electronics and Electrical Engineering

D-Index
62
Citations
20054
World Ranking
1414
National Ranking
581

Research.com Recognitions

  • 2020 - ACM Fellow For contributions to software and hardware design for power-efficient computer architectures

Overview

David Brooks is affiliated with Harvard University in the United States and has contributed extensively to the fields of computer science and engineering. Their research spans across several subfields including electrical and electronic engineering, hardware and architecture, artificial intelligence, computer vision and pattern recognition, and computer networks and communications.

The scientist's work focuses on a variety of topics related to computing and technology. These include:

  • Parallel Computing and Optimization Techniques
  • Advanced Neural Network Applications
  • Advanced Memory and Neural Computing
  • Embedded Systems Design Techniques
  • Ferroelectric and Negative Capacitance Devices
  • Green IT and Sustainability
  • Cloud Computing and Resource Management

David Brooks has published papers in numerous venues, with frequently appearing outlets such as:

  • arXiv (Cornell University)
  • IEEE Micro
  • IEEE Journal of Solid-State Circuits
  • ACM Transactions on Architecture and Code Optimization
  • ACM SIGEnergy Energy Informatics Review

Some of their recent papers include the following:

  • Chasing Carbon: The Elusive Environmental Footprint of Computing, 2022, IEEE Micro
  • SMAUG, 2020, ACM Transactions on Architecture and Code Optimization
  • DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference, 2020, arXiv (Cornell University)
  • The Sky Is Not the Limit: A Visual Performance Model for Cyber-Physical Co-Design in Autonomous Machines, 2020, IEEE Computer Architecture Letters
  • Bridging Python to Silicon: The SODA Toolchain, 2022, IEEE Micro

Frequent co-authors who have collaborated with David Brooks include:

  • Gu-Yeon Wei
  • Carole-Jean Wu
  • Udit Gupta
  • Tianyu Jia
  • Samuel Hsia

In recognition of their work, David Brooks was named an ACM Fellow in 2020. The award citation highlighted contributions to software and hardware design for power-efficient computer architectures.

Best Publications

  • Wattch: a framework for architectural-level power analysis and optimizations

    David Brooks;Vivek Tiwari;Margaret Martonosi

  • Dynamic thermal management for high-performance microprocessors

    D. Brooks;M. Martonosi

  • System level analysis of fast, per-core DVFS using on-chip switching regulators

    Wonyoung Kim;M.S. Gupta;Gu-Yeon Wei;D. Brooks

  • Power-aware microarchitecture: design and modeling challenges for next-generation microprocessors

    D.M. Brooks;P. Bose;S.E. Schuster;H. Jacobson

  • Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

    Kim Hazelwood;Sarah Bird;David Brooks;Soumith Chintala

  • Accurate and efficient regression modeling for microarchitectural performance and power prediction

    Benjamin C. Lee;David M. Brooks

  • Minerva: enabling low-power, highly-accurate deep neural network accelerators

    Brandon Reagen;Paul Whatmough;Robert Adolf;Saketh Rama

  • Profiling a Warehouse-Scale Computer

    Svilen Kanev;Juan Pablo Darago;Kim Hazelwood;Parthasarathy Ranganathan

  • Machine Learning at Facebook: Understanding Inference at the Edge

    Carole-Jean Wu;David Brooks;Kevin Chen;Douglas Chen

  • Dynamically exploiting narrow width operands to improve processor power and performance

    D. Brooks;M. Martonosi

  • Process Variation Tolerant 3T1D-Based Cache Architectures

    Xiaoyao Liang;Ramon Canal;Gu-Yeon Wei;David Brooks

  • Thread motion: fine-grained power management for multi-core systems

    Krishna K. Rangan;Gu-Yeon Wei;David Brooks

  • Sustainable AI: Environmental Implications, Challenges and Opportunities.

    Carole-Jean Wu;Ramya Raghavendra;Udit Gupta;Bilge Acun

  • A Fully-Integrated 3-Level DC-DC Converter for Nanosecond-Scale DVFS

    Wonyoung Kim;D. Brooks;Gu-Yeon Wei

  • MachSuite: Benchmarks for accelerator design and customized architectures

    Brandon Reagen;Robert Adolf;Yakun Sophia Shao;Gu-Yeon Wei

  • Aladdin: a Pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures

    Yakun Sophia Shao;Brandon Reagen;Gu-Yeon Wei;David Brooks

  • A Dynamic Compilation Framework for Controlling Microprocessor Energy and Performance

    Qiang Wu;Margaret Martonosi;Douglas W. Clark;V. J. Reddi

  • Methods of inference and learning for performance modeling of parallel applications

    Benjamin C. Lee;David M. Brooks;Bronis R. de Supinski;Martin Schulz

  • An adaptive issue queue for reduced power at high performance

    Alper Buyuktosunoglu;Stanley Schuster;David Brooks;Pradip Bose

  • Chasing Carbon: The Elusive Environmental Footprint of Computing

    Udit Gupta;Young Geun Kim;Sylvia Lee;Jordan Tse

  • The Architectural Implications of Facebook's DNN-Based Personalized Recommendation

    Udit Gupta;Carole-Jean Wu;Xiaodong Wang;Maxim Naumov

  • Ares: a framework for quantifying the resilience of deep neural networks

    Brandon Reagen;Udit Gupta;Lillian Pentecost;Paul Whatmough

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

Frequent Co-Authors

Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Vijay Janapa Reddi
Vijay Janapa Reddi Harvard University
Pradip Bose
Pradip Bose IBM (United States)
Margaret Martonosi
Margaret Martonosi Princeton University
Kim Hazelwood
Kim Hazelwood Facebook (United States)
Carole-Jean Wu
Carole-Jean Wu Meta Platforms, Inc.
Kevin Skadron
Kevin Skadron University of Virginia
Alper Buyuktosunoglu
Alper Buyuktosunoglu IBM (United States)
Michael J. Lyons
Michael J. Lyons Boston University
David H. Albonesi
David H. Albonesi Cornell University

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