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Computer Science

D-Index
30
Citations
5442
World Ranking
13934
National Ranking
5541

Overview

R. Iris Bahar is affiliated with Brown University in the United States and has a research focus spanning computer science and engineering with a particular emphasis on electrical and electronic engineering, computer vision and pattern recognition, artificial intelligence, hardware and architecture, and aerospace engineering. Their work covers a broad range of fields that include both theoretical and applied aspects of these disciplines.

The scientist's recent publications demonstrate active engagement in topics such as advanced neural network applications, parallel computing and optimization techniques, low-power high-performance VLSI design, semiconductor materials and devices, robotics and sensor-based localization, advancements in semiconductor devices and circuit design, and advanced image and video retrieval techniques.

Recent papers authored or coauthored by R. Iris Bahar include:

  • Workshops on Extreme Scale Design Automation (ESDA) Challenges and Opportunities for 2025 and Beyond, 2020, arXiv (Cornell University)
  • Fundamental Thermal Limits on Data Retention in Low-Voltage CMOS Latches and SRAM, 2020, IEEE Transactions on Device and Materials Reliability
  • A Reconfigurable Hardware Library for Robot Scene Perception, 2022, Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
  • Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation, 2020, arXiv (Cornell University)
  • Voltage Noise Mitigation With Barrier Approximation, 2020, IEEE Computer Architecture Letters

Frequent coauthors include:

  • Yanqi Liu
  • K. Semir Tatlidil
  • Steven A. Sloman
  • Elahe Rezaei
  • Marco Donato

Their work has been published primarily in venues such as:

  • arXiv (Cornell University)
  • IEEE Computer Architecture Letters
  • IEEE Transactions on Device and Materials Reliability
  • Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
  • 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

Best Publications

  • Algebric Decision Diagrams and Their Applications

    R. I. Bahar;E. A. Frohm;C. M. Gaona;G. D. Hachtel

  • Algebraic decision diagrams and their applications

    R. Iris Bahar;Erica A. Frohm;Charles M. Gaona;Gary D. Hachtel

  • Power and energy reduction via pipeline balancing

    R. Iris Bahar;Srilatha Manne

  • DRUM: A Dynamic Range Unbiased Multiplier for Approximate Applications

    Soheil Hashemi;R. Iris Bahar;Sherief Reda

  • ABACUS: a technique for automated behavioral synthesis of approximate computing circuits

    Kumud Nepal;Yueting Li;R. Iris Bahar;Sherief Reda

  • Power and performance tradeoffs using various caching strategies

    R. Iris Bahar;Gianluca Albera;Srilatha Manne

  • A Probabilistic-Based Design Methodology for Nanoscale Computation

    R. Iris Bahar;Joseph Mundy;Jie Chen

  • Understanding the impact of precision quantization on the accuracy and energy of neural networks

    Soheil Hashemi;Nicholas Anthony;Hokchhay Tann;R. Iris Bahar

  • Architectures for silicon nanoelectronics and beyond

    R.I. Bahar;C. Lau;D. Hammerstrom;D. Marculescu

  • Designing logic circuits for probabilistic computation in the presence of noise

    K. Nepal;R. I. Bahar;J. Mundy;W. R. Patterson

  • Dynamically reconfiguring processor resources to reduce power consumption in high-performance processors

    Roberto Maro;Yu Bai;R. Iris Bahar

  • Runtime configurable deep neural networks for energy-accuracy trade-off

    Hokchhay Tann;Soheil Hashemi;R. Iris Bahar;Sherief Reda

  • Automated High-Level Generation of Low-Power Approximate Computing Circuits

    Kumud Nepal;Soheil Hashemi;Hokchhay Tann;R. Iris Bahar

  • Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks

    Hokchhay Tann;Soheil Hashemi;R. Iris Bahar;Sherief Reda

  • Nano, Quantum and Molecular Computing: Implications to High Level Design and Validation

    Sandeep K. Shukla;R. Iris Bahar

  • A symbolic method to reduce power consumption of circuits containing false paths

    R. Iris Bahar;Gary D. Hachtel;Enrico Macii;Fabio Somenzi

  • Parametric yield management for 3D ICs: Models and strategies for improvement

    Cesare Ferri;Sherief Reda;R. Iris Bahar

  • Embedded-TM: Energy and complexity-effective hardware transactional memory for embedded multicore systems

    Cesare Ferri;Samantha Wood;Tali Moreshet;R. Iris Bahar

  • Energy reduction in multiprocessor systems using transactional memory

    Tali Moreshet;R. Iris Bahar;Maurice Herlihy

  • A low-power dynamic divider for approximate applications

    Soheil Hashemi;R. Iris Bahar;Sherief Reda

  • Computing the Maximum Power Cycles of a Sequential Circuit

    Srilatha Manne;Abelardo Pardo;R. Iris Bahar;Gary D. Hachtel

Frequent Co-Authors

Sherief Reda
Sherief Reda Brown University
Maurice Herlihy
Maurice Herlihy Brown University
Luca Benini
Luca Benini ETH Zurich
Enrico Macii
Enrico Macii Polytechnic University of Turin
Massimo Poncino
Massimo Poncino Polytechnic University of Turin
Joseph L. Mundy
Joseph L. Mundy Brown University
Fabio Somenzi
Fabio Somenzi University of Colorado Boulder
Gary D. Hachtel
Gary D. Hachtel University of Colorado Boulder
David Z. Pan
David Z. Pan The University of Texas at Austin
David Atienza
David Atienza École Polytechnique Fédérale de Lausanne

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