Yehua Ling

About

Yehua Ling is a researcher at the forefront of hardware acceleration for real-time computer vision, with a primary focus on stereo estimation for autonomous systems. Their major contributions center on developing resource-efficient FPGA-based accelerators that enable deep neural networks (DNNs) to achieve both high accuracy and real-time performance—a critical challenge for mobile robots and self-driving cars. Ling’s seminal work, "StereoEngine" (2020, 45 citations), pioneered the use of binary neural networks to dramatically reduce storage and energy demands while maintaining stereo estimation quality, setting a benchmark in the field. This was refined in "Lite-Stereo" (2022, 9 citations), which further optimized resource efficiency, and extended in their latest work on robust, real-time multi-baseline stereo accelerators (2023). Beyond vision, Ling has also contributed to medical device design, notably a flexible head fixation system for ophthalmic microsurgery (2017, 7 citations), demonstrating versatility in solving precision-dependent problems. With a growing citation impact and a clear trajectory toward making DNN-based perception practical for edge devices, Ling’s work is essential reading for students and engineers building the next generation of autonomous and surgical systems.

Research Focus

Key Achievements

Top Papers

1. 1
   StereoEngine: An FPGA-Based Accelerator for Real-Time High-Quality Stereo Estimation With Binary Neural Network

   45 citations · 2020
2. 2
   Lite-Stereo: A Resource-Efficient Hardware Accelerator for Real-Time High-Quality Stereo Estimation Using Binary Neural Network

   9 citations · 2022
3. 3
   A flexible head fixation for ophthalmic microsurgery

   7 citations · 2017
4. 4
   A robust and real-time DNN-based multi-baseline stereo accelerator in FPGAs

   3 citations · 2023

Key Collaborators

Contact & Links