Jiyu Chen
Papers
1
Total Citations
3
H-Index
1
About
Jiyu Chen is a researcher focused on efficient deep learning deployment for edge and mobile platforms, with a particular emphasis on hardware acceleration using field-programmable gate arrays (FPGAs). His most cited work, "An FPGA-Based Low-Power Mobile-NetV2 Accelerator" (2023), addresses a critical challenge in robotics and edge computing: the high computational and memory demands of convolutional neural networks (CNNs) like MobileNetV2. By designing a specialized FPGA accelerator, Chen demonstrates how to significantly reduce power consumption while maintaining inference performance, enabling advanced AI capabilities on resource-constrained devices. This contribution is vital for real-time applications in autonomous systems, drones, and portable electronics. With 3 citations to date, his work is gaining traction among researchers seeking practical solutions for low-power, high-efficiency neural network deployment. Chen’s research bridges the gap between algorithmic innovation and hardware implementation, making him a promising voice in the field of embedded AI and edge computing.
Research Focus
Key Achievements
Top Papers
- 1An FPGA-Based Low-Power Mobile-NetV2 Accelerator3 citations · 2023