Ying-chun Liu
Senior Engineer at Linaro
Taoyuan City, Taiwan
Summary
🤩
Rockstar🎓
Top SchoolYing-chun Liu is a senior engineer with 16 years of hands-on experience in C/C++ application and embedded systems development, specializing in Linux, drivers, and platform bring-up. Based in Taoyuan City, Taiwan, he has driven hardware enablement and boot/TEE work at Linaro, contributing notable support for Raspberry Pi 3 in Trusted Firmware-A and platform ports in OP-TEE. His open-source contributions span low-level board support, GPIO/SDHost drivers, and Trusted Board Boot integration, alongside applying ML at the edge by adding USB/Nest camera support and a TFLite YOLOv4 engine for BerryNet. Comfortable across kernel, bootloader, and Android build chains, he also implemented SystemReady/fastboot features and production trusted-boot flows for Mbed Linux. Colleagues rely on him for complex board bring-ups and pragmatic fixes that upstream cleanly, reflecting both deep systems knowledge and a penchant for shipping reliable embedded solutions.
16 years of coding experience
9 years of employment as a software developer
Master, Computer Science, Master, Computer Science at National Chiao Tung University
Github Skills (39)
bootloader10
architecture10
firmware10
c1110
c1710
device-tree10
deep-learning10
tensorflow10
soc10
sys10
dt10
arm10
computer-vision10
embedded10
tflite10
Programming languages (11)
Github contributions (5)
DT42/BerryNet
Jun 2017 - Dec 2020
Deep learning gateway on Raspberry Pi and other edge devices
Role in this project:
ML Engineer
ML EngineerContributions:80 commits, 15 PRs, 28 pushes in 3 years 6 months
Contributions summary:Ying-Chun Liu (PaulLiu) contributed significantly to the `berrynet` project, which focuses on deep learning on edge devices. They implemented support for USB cameras, including both snapshot and streaming functionalities, using JavaScript and OpenCV within the camera client. Furthermore, the user added the integration for the Nest IP camera and developed a Caffe2 classification server. They also introduced a TFLite-based YOLOv4 engine for object detection, demonstrating expertise in deploying and integrating machine learning models.
raspberry-piedge-computingdeep-learningedge-devicesraspberry
ARM-software/arm-trusted-firmware
Jun 2018 - Nov 2021
Read-only mirror of Trusted Firmware-A
Role in this project:Embedded Systems Engineer / IoT Developer
Embedded Systems Engineer / IoT DeveloperContributions:30 commits, 8 PRs, 18 comments in 3 years 5 months
Contributions summary:Ying-chun primarily contributed to adding and improving support for the Raspberry Pi 3 platform within the Trusted Firmware-A repository. Their work included integrating OP-TEE support, enabling Trusted Board Boot functionality, and adding a GPIO driver with pinmux selections. The user also focused on SDHost driver enhancements for improved SD card read/write operations.
trustedread-onlyfirmwareembedded
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