Industry Webinar

Software and Cybersecurity Challenges for Robust Smart Camera Applications Solved with AMD-Xilinx Kria SOMs

Julien Bernet Security Manager | Witekio

ABOUT

Julien is a vulnerability research engineer. After a PhD in theoretical computer science, he’s been working for various security labs with a focus on software security for embedded devices and smart cards. He’s been involved in many security evaluations and has a soft spot for the use of formal methods in software security. He is now Security Manager at Witekio, in charge of the development of the internal security framework and defining the security architecture for developments.

Mario Bergeron Machine Learning Specialist | Avnet

ABOUT

Mario Bergeron is a Technical Marketing Engineer for Avnet, specializing in embedded vision and machine learning. Mario has a Bachelor of Science degree in Computer Engineering from Université Laval in Québec City. He started his career as an ASIC designer and has since then accumulated over 30 years of DSP and FPGA based embedded design experience. Present activities include deep learning platforms, reference designs, and customer training.

Software and Cybersecurity Challenges for Robust Smart Camera Applications Solved with AMD-Xilinx Kria SOMs

Explore how to setup robust vision systems with Machine Learning (ML) and implement the appropriate security requirements and best practices to develop secured software and ML on SOMs.

In partnership with Avnet

With vision systems rapidly evolving to keep up with the demands of increasingly complex applications, total system performance and security are some of the top concerns. AMD-Xilinx is addressing these concerns with their Kria SOMs which acquire and preprocess video, run inference and stream results with an integrated GStreamer pipeline without HDL coding. All of this is done while maintaining all security requirements.

Join our experts who will present a tutorial based on AMD-Xilinx Kria SOM that explains how to setup robust vision systems with Machine Learning (ML) and implement the appropriate security requirements and best practices to develop secured software and ML on SOMs.