54 citations · 135 across the 17 of their papers we have counts for
9 papers · 1 filter
WinoCNN: Kernel Sharing Winograd Systolic Array for Efficient Convolutional Neural Network Acceleration on FPGAs
Xinheng Liu, Yao Chen, Cong Hao +2
The combination of Winograd's algorithm and systolic array architecture has demonstrated the capability of improving DSP efficiency in accelerating convolutional neural networks (C…
Skew-Oblivious Data Routing for Data-Intensive Applications on FPGAs with HLS
Xinyu Chen, Hongshi Tan, Yao Chen +3
FPGAs have become emerging computing infrastructures for accelerating applications in datacenters. Meanwhile, high-level synthesis (HLS) tools have been proposed to ease the progra…
Being-ahead: Benchmarking and Exploring Accelerators for Hardware-Efficient AI Deployment
Xiaofan Zhang, Hanchen Ye, Deming Chen
Customized hardware accelerators have been developed to provide improved performance and efficiency for DNN inference and training. However, the existing hardware accelerators may…
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Cong Hao, Jordan Dotzel, Jinjun Xiong +3
Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives. Empowered by edge computing, AI workload…
F-CAD: A Framework to Explore Hardware Accelerators for Codec Avatar Decoding
Xiaofan Zhang, Dawei Wang, Pierce Chuang +3
Creating virtual avatars with realistic rendering is one of the most essential and challenging tasks to provide highly immersive virtual reality (VR) experiences. It requires not o…
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
Cong Hao, Yao Chen, Xiaofan Zhang +4
High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators. To improve the overall solution quality…