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Vivienne Sze

Vivienne Sze

D-Index & Metrics

Computer Science

D-Index
49
Citations
21293
World Ranking
5746
National Ranking
2611

Overview

Vivienne Sze is a researcher affiliated with MIT in the United States. Their academic work spans the fields of Computer Science and Engineering, with a focused involvement in subfields such as Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Aerospace Engineering, Artificial Intelligence, and Computer Networks and Communications.

The main topics covered by Vivienne Sze's research include Robotics and Sensor-Based Localization, Advanced Vision and Imaging, Advanced Neural Network Applications, Advanced Memory and Neural Computing, Ferroelectric and Negative Capacitance Devices, Robotic Path Planning Algorithms, and Gaze Tracking and Assistive Technology.

Some of the recent papers authored or co-authored by Vivienne Sze include:

  • Efficient Processing of Deep Neural Networks, 2020, Synthesis Lectures on Computer Architecture
  • How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful, 2020, IEEE Solid-State Circuits Magazine
  • Data Centers on Wheels: Emissions From Computing Onboard Autonomous Vehicles, 2022, IEEE Micro
  • FSMI: Fast computation of Shannon mutual information for information-theoretic mapping, 2020, The International Journal of Robotics Research
  • Searching for Efficient Multi-Stage Vision Transformers, 2021, arXiv (Cornell University)

Vivienne Sze has collaborated frequently with several co-authors, including Joel Emer, Tien-Ju Yang, Yu-Hsin Chen, Sertaç Karaman, and Peter Zhi Xuan Li. These collaborations have contributed to a variety of publications and research outputs over time.

Their work has been published in multiple venues, with notable appearances in arXiv (Cornell University), IEEE Solid-State Circuits Magazine, the 2022 International Conference on Robotics and Automation (ICRA), IEEE Robotics and Automation Letters, and Synthesis Lectures on Computer Architecture.

In addition to journal articles and conference papers, Vivienne Sze is credited with book publications through Morgan & Claypool Publishers, which includes the title Efficient Processing of Deep Neural Networks published in 2020.

Best Publications

  • Efficient Processing of Deep Neural Networks: A Tutorial and Survey

    Vivienne Sze;Yu-Hsin Chen;Tien-Ju Yang;Joel S. Emer

  • Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

    Yu-Hsin Chen;Tushar Krishna;Joel S. Emer;Vivienne Sze

  • 14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

    Yu-Hsin Chen;Tushar Krishna;Joel Emer;Vivienne Sze

  • Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks

    Yu-Hsin Chen;Joel Emer;Vivienne Sze

  • Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices

    Yu-Hsin Chen;Tien-Ju Yang;Joel S. Emer;Vivienne Sze

  • Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

    Tien-Ju Yang;Yu-Hsin Chen;Vivienne Sze

  • High Efficiency Video Coding (HEVC)

    Vivienne Sze;Madhukar Budagavi;Gary J. Sullivan

  • NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

    Tien-Ju Yang;Andrew G. Howard;Bo Chen;Xiao Zhang

  • High Throughput CABAC Entropy Coding in HEVC

    V. Sze;M. Budagavi

  • Hardware for machine learning: Challenges and opportunities

    Vivienne Sze;Yu-Hsin Chen;Joel Einer;Amr Suleiman

  • FastDepth: Fast Monocular Depth Estimation on Embedded Systems

    Diana Wofk;Fangchang Ma;Tien-Ju Yang;Sertac Karaman

  • Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

    Yu-Hsin Chen;Joel S. Emer;Vivienne Sze

  • High Efficiency Video Coding (HEVC): Algorithms and Architectures

    Vivienne Sze;Madhukar Budagavi;Gary J. Sullivan

  • DeeperLab: Single-Shot Image Parser

    Tien-Ju Yang;Maxwell D. Collins;Yukun Zhu;Jyh-Jing Hwang

  • Core Transform Design in the High Efficiency Video Coding (HEVC) Standard

    Madhukar Budagavi;Arild Fuldseth;Gisle Bjontegaard;Vivienne Sze

  • A method to estimate the energy consumption of deep neural networks

    Tien-Ju Yang;Yu-Hsin Chen;Joel Emer;Vivienne Sze

  • Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs

    Yannan Nellie Wu;Joel S. Emer;Vivienne Sze

  • Efficient Processing of Deep Neural Networks

    Vivienne Sze;Yu-Hsin Chen;Tien-Ju Yang;Joel S. Emer

  • Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators

    Yu-Hsin Chen;Joel Emer;Vivienne Sze

  • Navion: A 2-mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones

    Amr Suleiman;Zhengdong Zhang;Luca Carlone;Sertac Karaman

  • Hardware for Machine Learning: Challenges and Opportunities

    Vivienne Sze;Yu-Hsin Chen;Joel Emer;Amr Suleiman

  • Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

    Yu-Hsin Chen;Joel Emer;Vivienne Sze

Frequent Co-Authors

Gary J. Sullivan
Gary J. Sullivan Microsoft (United States)
Naveen Verma
Naveen Verma Princeton University
Patrick P. Mercier
Patrick P. Mercier University of California, San Diego
David D. Wentzloff
David D. Wentzloff University of Michigan–Ann Arbor

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