World's Best Scientists 2026 revealed!
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Electronics and Electrical Engineering
USA
2026

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
109
Citations
44367
World Ranking
112
National Ranking
53

Research.com Recognitions

  • 2026 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 2025 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 2016 - Semiconductor Industry Association University Researcher Award

Overview

David Blaauw is affiliated with the University of Michigan-Ann Arbor in the United States. Their research spans multiple fields, primarily focusing on engineering and computer science, with significant contributions in electrical and electronic engineering, molecular biology, biomedical engineering, cellular and molecular neuroscience, and artificial intelligence.

The scientist's work encompasses diverse topics, including:

  • Advanced Memory and Neural Computing
  • Genomics and Phylogenetic Studies
  • Neuroscience and Neural Engineering
  • Analog and Mixed-Signal Circuit Design
  • Low-power High-Performance VLSI Design
  • CCD and CMOS Imaging Sensors
  • Parallel Computing and Optimization Techniques

Recent notable publications include:

  • "A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces," 2020, Nature Biomedical Engineering
  • "SquiggleNet: real-time, direct classification of nanopore signals," 2021, Genome Biology
  • "Bayesian Inference of RNA Velocity from Multi-Lineage Single-Cell Data," 2022, bioRxiv (Cold Spring Harbor Laboratory)
  • "A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry," 2022, IEEE Journal of Solid-State Circuits
  • "Bridging the "Last Millimeter" Gap of Brain-Machine Interfaces via Near-Infrared Wireless Power Transfer and Data Communications," 2021, ACS Photonics

Blaauw frequently publishes in venues such as:

  • IEEE Journal of Solid-State Circuits
  • IEEE Solid-State Circuits Letters
  • bioRxiv (Cold Spring Harbor Laboratory)
  • 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
  • arXiv (Cornell University)

The scientist collaborates regularly with researchers including Hun-Seok Kim, Dennis Sylvester, Hyochan An, Reetuparna Das, and Satish Narayanasamy.

Among their recognitions, Blaauw received the Semiconductor Industry Association University Researcher Award in 2016.

Best Publications

  • Razor: a low-power pipeline based on circuit-level timing speculation

    Dan Ernst;Nam Sung Kim;Shidhartha Das;Sanjay Pant

  • Leakage current: Moore's law meets static power

    N.S. Kim;T. Austin;D. Baauw;T. Mudge

  • Drowsy caches: simple techniques for reducing leakage power

    Krisztián Flautner;Nam Sung Kim;Steve Martin;David Blaauw

  • Near-Threshold Computing: Reclaiming Moore's Law Through Energy Efficient Integrated Circuits

    R.G. Dreslinski;M. Wieckowski;D. Blaauw;D. Sylvester

  • RazorII: In Situ Error Detection and Correction for PVT and SER Tolerance

    S. Das;C. Tokunaga;S. Pant;W.-H. Ma

  • Combined dynamic voltage scaling and adaptive body biasing for lower power microprocessors under dynamic workloads

    Steven M. Martin;Krisztian Flautner;Trevor Mudge;David Blaauw

  • Statistical Timing Analysis for Intra-Die Process Variations with Spatial Correlations

    Aseem Agarwal;David Blaauw;Vladimir Zolotov

  • A self-tuning DVS processor using delay-error detection and correction

    S. Das;D. Roberts;Seokwoo Lee;S. Pant

  • Theoretical and practical limits of dynamic voltage scaling

    Bo Zhai;David Blaauw;Dennis Sylvester;Krisztian Flautner

  • Razor: circuit-level correction of timing errors for low-power operation

    D. Ernst;S. Das;S. Lee;D. Blaauw

  • Statistical Timing Analysis: From Basic Principles to State of the Art

    D. Blaauw;K. Chopra;A. Srivastava;L. Scheffer

  • A Portable 2-Transistor Picowatt Temperature-Compensated Voltage Reference Operating at 0.5 V

    Mingoo Seok;Gyouho Kim;D. Blaauw;D. Sylvester

  • Hierarchical analysis of power distribution networks

    M. Zhao;R.V. Panda;S.S. Sapatnekar;D. Blaauw

  • Statistical Analysis and Optimization for VLSI: Timing and Power

    Ashish Srivastava;Dennis Sylvester;David Blaauw

  • A 28 nm Configurable Memory (TCAM/BCAM/SRAM) Using Push-Rule 6T Bit Cell Enabling Logic-in-Memory

    Supreet Jeloka;Naveen Bharathwaj Akesh;Dennis Sylvester;David Blaauw

  • A highly resilient routing algorithm for fault-tolerant NoCs

    David Fick;Andrew DeOrio;Gregory Chen;Valeria Bertacco

  • Analysis and mitigation of variability in subthreshold design

    Bo Zhai;Scott Hanson;David Blaauw;Dennis Sylvester

  • Compute Caches

    Shaizeen Aga;Supreet Jeloka;Arun Subramaniyan;Satish Narayanasamy

  • Ultralow-voltage, minimum-energy CMOS

    S. Hanson;B. Zhai;K. Bernstein;D. Blaauw

  • Razor II: In Situ Error Detection and Correction for PVT and SER Tolerance

    D. Blaauw;S. Kalaiselvan;K. Lai;Wei-Hsiang Ma

  • A cubic-millimeter energy-autonomous wireless intraocular pressure monitor

    G Chen;H Ghaed;R Haque;M Wieckowski

  • Hierarchical analysis of power distribution networks

    Min Zhao;Rajendran V. Panda;Sachin S. Sapatnekar;Tim Edwards

Frequent Co-Authors

Dennis Sylvester
Dennis Sylvester University of Michigan–Ann Arbor
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Yoonmyung Lee
Yoonmyung Lee Sungkyunkwan University
Mingoo Seok
Mingoo Seok Columbia University
Krisztian Flautner
Krisztian Flautner University of Michigan–Ann Arbor
Todd Austin
Todd Austin University of Michigan–Ann Arbor
Sarma Vrudhula
Sarma Vrudhula Arizona State University
Prabal Dutta
Prabal Dutta University of California, Berkeley
Massimo Alioto
Massimo Alioto National University of Singapore
Chaitali Chakrabarti
Chaitali Chakrabarti Arizona State University

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Related Online Degrees & Career Pathways

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Additionally, competency based master's degree programs provide a personalized learning approach, allowing students to progress by demonstrating mastery of skills. This model is particularly effective for engineers seeking to specialize or update their knowledge efficiently.

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