168 citations · 477 across the 22 of their papers we have counts for
5 papers · 1 filter
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…
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…
DNNExplorer: A Framework for Modeling and Exploring a Novel Paradigm of FPGA-based DNN Accelerator
Xiaofan Zhang, Hanchen Ye, Junsong Wang +4
Existing FPGA-based DNN accelerators typically fall into two design paradigms. Either they adopt a generic reusable architecture to support different DNN networks but leave some pe…
HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and Implementation
Hanchen Ye, Xiaofan Zhang, Zhize Huang +2
To speedup Deep Neural Networks (DNN) accelerator design and enable effective implementation, we propose HybridDNN, a framework for building high-performance hybrid DNN accelerator…