World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5052
World Ranking
12620
National Ranking
5110

Electronics and Electrical Engineering

D-Index
33
Citations
5070
World Ranking
6010
National Ranking
2009

Overview

Zhengya Zhang is a researcher affiliated with the University of Michigan-Ann Arbor in the United States. Their body of work spans multiple fields within engineering and computer science, with a focus on electrical and electronic engineering as well as several related subfields.

The main fields of study Zhang has contributed to include:

  • Engineering
  • Computer Science

Within these broad fields, their work engages with specialized subfields such as:

  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Biomedical Engineering
  • Hardware and Architecture

Zhang's research topics cover areas including:

  • Advanced Memory and Neural Computing
  • CCD and CMOS Imaging Sensors
  • Advanced Neural Network Applications
  • Error Correcting Code Techniques
  • Advanced Wireless Communication Techniques
  • Ferroelectric and Negative Capacitance Devices
  • Parallel Computing and Optimization Techniques

Their work is published frequently in established venues such as:

  • IEEE Journal of Solid-State Circuits
  • arXiv (Cornell University)
  • 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
  • IEEE Transactions on Circuits and Systems I Regular Papers
  • Scientific Reports

Recent papers authored or co-authored by Zhengya Zhang include:

  • "SNAP: An Efficient Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network Inference" (2020), published in IEEE Journal of Solid-State Circuits
  • "A Configurable Successive-Cancellation List Polar Decoder Using Split-Tree Architecture" (2020), published in IEEE Journal of Solid-State Circuits
  • "An 8-bit 20.7 TOPS/W Multi-Level Cell ReRAM-based Compute Engine" (2022), published in 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
  • "A Fully Integrated Reprogrammable CMOS-RRAM Compute-in-Memory Coprocessor for Neuromorphic Applications" (2020), published in IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
  • "A 1.87-mm2 56.9-GOPS Accelerator for Solving Partial Differential Equations" (2020), published in IEEE Journal of Solid-State Circuits

Their frequent co-authors include:

  • Wei Tang
  • Yaoyu Tao
  • Michael P. Flynn
  • Wei Lü
  • Zhang Jie-Fang

Best Publications

  • A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations

    Fuxi Cai;Justin M. Correll;Seung Hwan Lee;Yong Lim;Yong Lim

  • Sparse coding with Memristor networks

    Wei Lu;Fuxi Cai;Patrick Sheridan;Chao Du

  • An Efficient 10GBASE-T Ethernet LDPC Decoder Design With Low Error Floors

    Zhengya Zhang;V. Anantharam;M.J. Wainwright;B. Nikolic

  • Analysis of Absorbing Sets and Fully Absorbing Sets of Array-Based LDPC Codes

    L. Dolecek;Zhengya Zhang;V. Anantharam;M.J. Wainwright

  • An Injectable 64 nW ECG Mixed-Signal SoC in 65 nm for Arrhythmia Monitoring

    Yen-Po Chen;Dongsuk Jeon;Yoonmyung Lee;Yejoong Kim

  • Design of LDPC decoders for improved low error rate performance: quantization and algorithm choices

    Zhengya Zhang;L. Dolecek;B. Nikolic;V. Anantharam

  • A Native Stochastic Computing Architecture Enabled by Memristors

    Phil Knag;Wei Lu;Zhengya Zhang

  • GEN03-6: Investigation of Error Floors of Structured Low-Density Parity-Check Codes by Hardware Emulation

    Zhengya Zhang;Lara Dolecek;Borivoje Nikolic;Venkat Anantharam

  • Lowering LDPC Error Floors by Postprocessing

    Zhengya Zhang;L. Dolecek;B. Nikolic;V. Anantharam

  • A 640M pixel/s 3.65mW sparse event-driven neuromorphic object recognition processor with on-chip learning

    Jung Kuk Kim;Phil Knag;Thomas Chen;Zhengya Zhang

  • Analysis of Absorbing Sets for Array-Based LDPC Codes

    L. Dolecek;Zhengya Zhang;V. Anantharam;M. Wainwright

  • A 4.68Gb/s belief propagation polar decoder with bit-splitting register file

    Youn Sung Park;Yaoyu Tao;Shuanghong Sun;Zhengya Zhang

  • Predicting error floors of structured LDPC codes: deterministic bounds and estimates

    L. Dolecek;P. Lee;Zhengya Zhang;V. Anantharam

  • Low-Power High-Throughput LDPC Decoder Using Non-Refresh Embedded DRAM

    Youn Sung Park;David Blaauw;Dennis Sylvester;Zhengya Zhang

  • A Sparse Coding Neural Network ASIC With On-Chip Learning for Feature Extraction and Encoding

    Phil Knag;Jung Kuk Kim;Thomas Chen;Zhengya Zhang

  • 24.3 An implantable 64nW ECG-monitoring mixed-signal SoC for arrhythmia diagnosis

    Dongsuk Jeon;Yen-Po Chen;Yoonmyung Lee;Yejoong Kim

  • SNAP: An Efficient Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network Inference

    Jie-Fang Zhang;Ching-En Lee;Chester Liu;Yakun Sophia Shao

  • CASCADE: Connecting RRAMs to Extend Analog Dataflow In An End-To-End In-Memory Processing Paradigm

    Teyuh Chou;Wei Tang;Jacob Botimer;Zhengya Zhang

  • Evaluation of the Low Frame Error Rate Performance of LDPC Codes Using Importance Sampling

    L. Dolecek;Zhengya Zhang;M. Wainwright;V. Anantharam

  • Quantization Effects in Low-Density Parity-Check Decoders

    Zhengya Zhang;L. Dolecek;M. Wainwright;V. Anantharam

  • A 3.43TOPS/W 48.9pJ/pixel 50.1nJ/classification 512 analog neuron sparse coding neural network with on-chip learning and classification in 40nm CMOS

    Fred N. Buhler;Peter Brown;Jiabo Li;Thomas Chen

  • Memristive devices for stochastic computing

    Siddharth Gaba;Phil Knag;Zhengya Zhang;Wei Lu

Frequent Co-Authors

Lara Dolecek
Lara Dolecek University of California, Los Angeles
Venkat Anantharam
Venkat Anantharam University of California, Berkeley
Borivoje Nikolic
Borivoje Nikolic University of California, Berkeley
Wei Lu
Wei Lu University of Michigan–Ann Arbor
Dennis Sylvester
Dennis Sylvester University of Michigan–Ann Arbor
David Blaauw
David Blaauw University of Michigan–Ann Arbor
Marios C. Papaefthymiou
Marios C. Papaefthymiou University of California, Irvine
Richard D. Wesel
Richard D. Wesel University of California, Los Angeles
Yoonmyung Lee
Yoonmyung Lee Sungkyunkwan University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Electronics and Electrical Engineering, exploring related online degrees can open doors to diverse career opportunities. Programs like the best online master's for teaching are ideal for those interested in combining technical expertise with education, preparing graduates to teach engineering concepts effectively.

Competency-based learning is another flexible option that allows students to progress at their own pace by demonstrating mastery in key areas. These competency based masters programs are particularly beneficial for working professionals seeking to balance studies with career demands.

Military spouses and dependents can access tailored support through specific online institutions, ensuring seamless enrollment and accommodations. Exploring online universities for military spouses can provide flexible scheduling and dedicated resources for this community.

Additionally, enrolling in programs with online colleges with weekly start dates offers increased flexibility, allowing students to begin their coursework without waiting for traditional semester timelines. This approach supports continuous learning and timely degree completion.

Best Scientists Citing Zhengya Zhang

Trending Scientists

Recently Published Articles