Yu‐Ching Su
Papers
1
Total Citations
1
H-Index
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About
Yu-Ching Su is a leading researcher in energy-efficient AI hardware, with a focus on convolutional neural network (CNN) processors for real-time computer vision. Their most cited work, “A 16nm 5.7TOPS CNN Processor Supporting Bi-Directional FPN for Small-Object Detection on High-Resolution Videos,” introduces a groundbreaking processor that achieves 5.7 tera-operations per second while supporting bi-directional feature pyramid networks (FPN). This innovation directly addresses the critical challenge of detecting small objects in high-resolution video streams—a life-saving capability for advanced driver-assistance systems (ADAS), autonomous vehicles, UAVs, and VR/AR applications. By enabling robust detection of distant objects, Su’s work enhances safety in intelligent systems, ensuring proper following distances and situational awareness. The processor’s design, implemented in 16nm technology, balances high throughput with energy efficiency, making it suitable for edge deployment. With growing citations, this research underscores Su’s impact on bridging algorithmic advances in object detection with practical, low-power hardware solutions, positioning them as a key contributor to the next generation of real-time AI systems.
Research Focus
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Top Papers
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