After 18 years, the Embedded System Design Contest (ESDC) is now one of China's high-profile collegiate STEM contest. In this invitation-only contest, undergraduate students across the world compete head to head to build innovative designs while cultivate innovation, teamwork and hands on engineering experience. Projects are based on the latest generation of Intel® embedded hardware and software tools. Started in 2002, this high-profile biannual contest has attracted 3000+ students from almost 100 universities worldwide, and influenced tens of thousands faculties and students.
Contest Timeline subjects to change, please refer to the official contest website for the final arrangement
Please refer to the official contest website at ESDC
Development of contest projects must be based on the following hardware:
AI-BoxX Gen.1 (WHL-U+HDDL-R8) – Required
DE10-Nano FPGA / DE10-Lite FPGA – Optional (up to one per team)
Heterogeneous Extensible Robot Open Platform (HERO) - Optional
A scalable AI computing platform architecture for IoT video, when teamed with VPU accelerator HDDL-R8 for deep learning with 8 Intel® Movidius™ Myriad™ X AI visual processing units, enables powerful deep learning inference capabilities and further enhance Intel’s end-to-end AI product portfolio.
Intel® Vision Accelerator with Intel® Movidius™ Vision Processing Units (VPU):
Whisky Lake (WHL) is the 2019 Core U series, optimized for embedded, retail and gaming verticals usages like IPC, HMI, digital signage, office automation, lottery, Pachinko and table gaming.
The Internet of Things is evolving along the path from interconnection to intelligence and from intelligence to autonomy. The combination of edge computing and artificial intelligence technology will be the development direction of the Internet of Things in the future. This training will introduce the evolution of the Internet of Things edge computing system, the challenges, solutions and future development trends of artificial intelligence-based edge computing systems.
This course mainly introduces the basic principles of algorithms of Convolutional Neural Network (CNN) and object detection for computer vision, and elaborates the installation and use of Intel’s open source OpenVINO™ platform for machine learning. Through hands on exercises, this course provides extensive introduction to the application of computer vision in the typical fields such as number plate recognition, intelligent traffic light control, smart classroom, hazardous commodity recognition and more.
Learn the Course
The HERO computing platform is a set of scalable open heterogeneous computing platforms designed and developed by Intel Labs China (ILC) for smart devices.
By utilize Intel computer vision inference and network optimization development kit OpenVINO on the HERO platform, users enjoy the rapid deployment of deep learning networks on different hardware accelerators in a convenient and efficient way to optimize system performance, applicable in the areas of smart robots, smart homes, smart retail, lightweight autonomous driving and more.
More technical resources can be found in the forum, community and WeChat*/QQ group:
• Leading edge Robot research at Intel Labs China and co-organized challenges/workshop
• Computer on Module introduction
• Robot Key technologies introduction
• Robot related application introduction
OpenVINO China Community (coming soon)
Connect to the Community via WeChat
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Revisão do aviso #20110804