Jinjun Xiong

Professor

University at Buffalo

United States

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Jinjun Xiong is a Professor at the University at Buffalo, USA. His research interests encompass artificial intelligence, machine learning, and hardware-software co-design, as demonstrated in his recent articles that explore interdisciplinary applications and efficiency in computational frameworks. His work includes contributions to fields such as image compression, deep neural networks, and heterogeneous systems.

Articles (16)

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2025

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2025

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2025

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2025

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2024

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2024

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2022

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2022

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2021

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2021

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2021

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2021

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2021

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2021

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2021

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2020

Collaborators (12)

Zhiru Zhang

Associate Professor

Cornell University

UNITED STATES

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Takashi Sato

教授 / Professor

Kyoto University

JAPAN

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Peipei Zhou

Assistant Professor

University of Pittsburgh

UNITED STATES

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Julie Kientz

Professor

University of Washington

UNITED STATES

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Deming Chen

Abel Bliss Professor of Engineering

University of Illinois at Urbana-Champaign

UNITED STATES

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Meng Wang

Rensselaer Polytechnic Institute

UNITED STATES

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Carol Miller

Professor

Pennsylvania State University

UNITED STATES

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Volodymyr Kindratenko

University of Illinois at Urbana-Champaign

UNITED STATES

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Hussam Amrouch

Professor and Head of the Chair of AI Processor Design

Technical University of Munich

GERMANY

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Masahiro Fujita

University of Tokyo

JAPAN

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Seyyedali Hosseinalipour

Assistant Professor

University at Buffalo

UNITED STATES

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Wen-mei Hwu

University of Illinois at Urbana-Champaign

UNITED STATES

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Jinjun Xiong

Research Interests

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Articles (16)

MTrain: Enable Efficient CNN Training on Heterogeneous FPGA-Based Edge Servers

Supporting Speech-Language Pathologists in Schools With Interdisciplinary Team Science: A Viewpoint From the National Artificial Intelligence Institute for Exceptional Education

Breaking the Barriers of One-to-One Usage of Implicit Neural Representation in Image Compression: A Linear Combination Approach With Performance Guarantees

SCOPE: Schoolbook-Originated Novel Polynomial Multiplication Accelerators for NTRU-Based PQC

ML-TCAD: Accelerating FeFET Reliability Analysis Using Machine Learning

CHEF: A Framework for Deploying Heterogeneous Models on Clusters With Heterogeneous FPGAs

Exploring HW/SW Co-Design for Video Analysis on CPU-FPGA Heterogeneous Systems

VisualNet: An End-to-End Human Visual System Inspired Framework to Reduce Inference Latency of Deep Neural Networks

Efficient Methods for Mapping Neural Machine Translator on FPGAs

Improved Linear Convergence of Training CNNs With Generalizability Guarantees: A One-Hidden-Layer Case

Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign

Guest Editors' Introduction: Machine Intelligence at the Edge

A co-design framework of neural networks and quantum circuits towards quantum advantage

Large graph convolutional network training with GPU-oriented data communication architecture

On Interpretability of Artificial Neural Networks: A Survey

Achieve data privacy and clustering accuracy simultaneously through quantized data recovery

Collaborators (12)

Zhiru Zhang

Takashi Sato

Peipei Zhou

Julie Kientz

Deming Chen

Meng Wang

Carol Miller

Volodymyr Kindratenko

Hussam Amrouch

Masahiro Fujita

Seyyedali Hosseinalipour

Wen-mei Hwu

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