Haiyang Chi
Papers
1
Total Citations
11
H-Index
1
About
Haiyang Chi is a researcher specializing in edge AI and lightweight deep learning architectures, with a particular focus on enabling intelligent object recognition on resource-constrained devices such as smartphones and smart educational products. His most cited work, "A Novel Fusion Pruning-Processed Lightweight CNN for Local Object Recognition on Resource-Constrained Devices" (2024, 11 citations), addresses a critical bottleneck in deploying large convolutional neural networks (CNNs) on consumer electronics with limited computational power and memory. Chi’s major contribution lies in developing a fusion pruning technique that systematically reduces model complexity while preserving recognition accuracy, making real-time local inference feasible without cloud dependency. This work has direct implications for advancing smart education tools and portable AI systems, bridging the gap between high-performance deep learning and practical, low-power hardware. By tackling the trade-off between model size and performance, Chi’s research paves the way for more accessible, efficient AI in everyday devices, marking him as an emerging voice in the field of embedded machine learning and edge computing.
Research Focus
Key Achievements
Top Papers
- 1