World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
8915
World Ranking
13834
National Ranking
1677

Overview

Tianshi Chen is affiliated with the Chinese Academy of Sciences in China. Their research spans primarily within the fields of Computer Science and Engineering, with a notable focus on subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Hardware and Architecture, and Control and Systems Engineering.

The core topics of their work encompass Advanced Neural Network Applications, Control Systems and Identification, Neural Networks and Applications, Advanced Memory and Neural Computing, Human Pose and Action Recognition, Model Reduction and Neural Networks, and Gaussian Processes and Bayesian Inference.

Chen's publication record includes contributions to several frequent venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Computers
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Automatica
  • IEEE Transactions on Image Processing

Their recent papers include:

  • "Distilling Object Detectors with Feature Richness," 2021, arXiv (Cornell University)
  • "DWM: A Decomposable Winograd Method for Convolution Acceleration," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "An efficient implementation for spatial-temporal Gaussian process regression and its applications," 2022, Automatica
  • "Machine Learning Computers With Fractal von Neumann Architecture," 2020, IEEE Transactions on Computers
  • "Addressing Irregularity in Sparse Neural Networks through a Cooperative Software/Hardware Approach," 2020, IEEE Transactions on Computers

Frequent co-authors collaborating with Chen include:

  • Zidong Du
  • Xishan Zhang
  • Shaoli Liu
  • Yunji Chen
  • Tian Zhi

Best Publications

  • DaDianNao: A Machine-Learning Supercomputer

    Yunji Chen;Tao Luo;Shaoli Liu;Shijin Zhang

  • DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning

    Tianshi Chen;Zidong Du;Ninghui Sun;Jia Wang

  • ShiDianNao: shifting vision processing closer to the sensor

    Zidong Du;Robert Fasthuber;Tianshi Chen;Paolo Ienne

  • Cambricon-X: An accelerator for sparse neural networks

    Unknown

  • Cambricon-x: an accelerator for sparse neural networks

    Shijin Zhang;Zidong Du;Lei Zhang;Huiying Lan

  • PuDianNao: A Polyvalent Machine Learning Accelerator

    Daofu Liu;Tianshi Chen;Shaoli Liu;Jinhong Zhou

  • Cambricon: an instruction set architecture for neural networks

    Shaoli Liu;Zidong Du;Jinhua Tao;Dong Han

  • DianNao

    Unknown

  • DianNao

    Unknown

  • DianNao family: energy-efficient hardware accelerators for machine learning

    Yunji Chen;Tianshi Chen;Zhiwei Xu;Ninghui Sun

  • DaDianNao: A Neural Network Supercomputer

    Tao Luo;Shaoli Liu;Ling Li;Yuqing Wang

  • Cambricon-s: addressing irregularity in sparse neural networks through a cooperative software/hardware approach

    Xuda Zhou;Zidong Du;Qi Guo;Shaoli Liu

  • A large population size can be unhelpful in evolutionary algorithms

    Tianshi Chen;Ke Tang;Guoliang Chen;Xin Yao

  • BenchNN: On the broad potential application scope of hardware neural network accelerators

    Tianshi Chen;Yunji Chen;Marc Duranton;Qi Guo

  • ShiDianNao

    Unknown

  • Cambricon: An Instruction Set Architecture for Neural Networks

    Unknown

  • Analysis of Computational Time of Simple Estimation of Distribution Algorithms

    Tianshi Chen;Ke Tang;Guoliang Chen;Xin Yao

  • Scaling Up Estimation of Distribution Algorithms for Continuous Optimization

    Weishan Dong;Tianshi Chen;Peter Tino;Xin Yao

  • A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems

    Tianshi Chen;Jun He;Guangzhong Sun;Guoliang Chen

  • A multi-objective approach to Redundancy Allocation Problem in parallel-series systems

    Zai Wang;Tianshi Chen;Ke Tang;Xin Yao

  • Neuromorphic accelerators: a comparison between neuroscience and machine-learning approaches

    Zidong Du;Daniel D Ben-Dayan Rubin;Yunji Chen;Liqiang Hel

  • PuDianNao

    Unknown

  • Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization

    Xin Yu;Ke Tang;Tianshi Chen;Xin Yao;Xin Yao

  • Effective and efficient microprocessor design space exploration using unlabeled design configurations

    Tianshi Chen;Yunji Chen;Qi Guo;Zhi-Hua Zhou

  • When is an estimation of distribution algorithm better than an evolutionary algorithm

    Tianshi Chen;Per Kristian Lehre;Ke Tang;Xin Yao

  • Deterministic Replay: A Survey

    Yunji Chen;Shijin Zhang;Qi Guo;Ling Li

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Olivier Temam
Olivier Temam DeepMind (United Kingdom)
Ke Tang
Ke Tang Southern University of Science and Technology
Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Peter Tino
Peter Tino University of Birmingham
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Haibo Chen
Haibo Chen Shanghai Jiao Tong University
Yuan Xie
Yuan Xie Hong Kong University of Science and Technology
Paolo Ienne
Paolo Ienne École Polytechnique Fédérale de Lausanne
Rui Zhang
Rui Zhang National University of Singapore

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