Research Projects

Artificial Intelligence

Learning in the Frequency Domain

Kai Xu, Fengbo Ren

BNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency

Yixing Li, Fengbo Ren

Light-Weight RetinaNet for Object Detection on Edge Devices

Yixing Li, Akshay Dua, Fengbo Ren

Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning

Yixing Li, Fengbo Ren

A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing

Yixing Li, Fengbo Ren

Hardware-friendly Deep Learning

BNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency

Yixing Li, Fengbo Ren

Light-Weight RetinaNet for Object Detection on Edge Devices

Yixing Li, Akshay Dua, Fengbo Ren

Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning

Yixing Li, Fengbo Ren

A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks

Yixing Li, Kai Xu, Fengbo Ren

A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing

Yixing Li, Fengbo Ren

Data-driven Compressive Sensing

CRA: A Generic Compression Ratio Adapter for End-to-end Data-driven Image Compressive Sensing Reconstruction Frameworks

Zhikang Zhang, Kai Xu, Fengbo Ren

LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction

Kai Xu, Zhikang Zhang, Fengbo Ren

CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing

Kai Xu, Fengbo Ren

A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing

Kai Xu, Yixing Li, Fengbo Ren

An Energy-efficient Compressive Sensing Framework Incorporating Online Dictionary Learning for Long-Term Wireless Health Monitoring

Kai Xu, Yixing Li, Fengbo Ren

Reconfigurable Computing

A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks

Yixing Li, Kai Xu, Fengbo Ren

Internet-of-things (IoT) and Edge Computing

Light-Weight RetinaNet for Object Detection on Edge Devices

Yixing Li, Akshay Dua, Fengbo Ren

Are FPGAs Suitable for Edge Computing?

Saman Biookaghazadeh, Ming Zhao, Fengbo Ren

Energy-Efficient VLSI Systems

A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing

Yixing Li, Fengbo Ren

Scalable VLSI Architectures for Sparse Approximation

Fengbo Ren

Research Projects

Artificial Intelligence

Learning in the Frequency Domain

BNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency

Light-Weight RetinaNet for Object Detection on Edge Devices

Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning

A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing

Hardware-friendly Deep Learning

BNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency

Light-Weight RetinaNet for Object Detection on Edge Devices

Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning

A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks

A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing

Data-driven Compressive Sensing

CRA: A Generic Compression Ratio Adapter for End-to-end Data-driven Image Compressive Sensing Reconstruction Frameworks

LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction

CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing

A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing

An Energy-efficient Compressive Sensing Framework Incorporating Online Dictionary Learning for Long-Term Wireless Health Monitoring

Reconfigurable Computing

A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks

Internet-of-things (IoT) and Edge Computing

Light-Weight RetinaNet for Object Detection on Edge Devices

Are FPGAs Suitable for Edge Computing?

Energy-Efficient VLSI Systems

A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing

Scalable VLSI Architectures for Sparse Approximation

ASU

Other Research Opportunities

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