Webinar – Quickly Solve IIoT Database and Factory Management Challenges
Webinar – Kickstart your AI & Embedded Vision projects on NXP based phyCORE i.MX 8M Plus
Webinar – Ensuring interoperability between STM32 MPUs and MCUs with PHYTEC STM32MP1-based SoM
Webinar – AI at the Edge: Implementing ML and Embedded Vision Applications
Webinar – The SOM: Evolution of the System on Module
Thank you to all who were able to attend our last webinar, The SOM: Evolution of the System on Module presented by our Managing Director, Thomas Walker.
Webinar – 5 Tips for Designing an Embedded System with PHYTEC
PHYTEC just completed its first webinar! Presented by our own Hardware Engineer, Johnathan Feuerstein, this webinar covered general recommendations and tips from PHYTEC for designing a carrier board around a PHYTEC System on Module.
phyCORE-AM57x PRU-ICSS Application Guide
PHYTEC is continually growing our developer resources with new documentation. Our latest addition includes a guide on how to use the PRU-ICSS co-processor on the phyCORE-AM57x System on Module.
Combining Inference, Real-Time Robotics, Machine Learning, and AI at the Embedded Edge with PHYTEC and TI Sitara™ Platforms
At the beginning of January a few of my colleagues and I attended CES. We were blown away with how prevalent AI, Machine Learning, millimeter wave, and 5G were at the show. Most of us, in the industry, feel entrenched with the importance of these trendy technologies but we were surprised to see them start to turn to a mainstream audience (consumers). We did notice many of the demonstrations and applications were running on power-hungry X86 systems, cloud compute systems, and custom built silicon. In fact, we rode in a self-driving Lyft and the first thing I noticed was the considerably loud fan noise of whatever machine was in the trunk. One can admire the massive compute power of these systems but at the same time also question their efficiency and physical scalability (scaling down, not up). Many silicon vendors, such as Texas Instruments, are trying to address this concern. Moving intelligence to the ‘Embedded Edge’ allows reduced data transferred over networks, reduced power consumption, distributed, and balanced computing.
phyKARL – AWS Machine Learning and PHYTEC
The Machine Learning at the Edge demo built by PHYTEC showcases a practical Object Classification implementation of Amazon Greengrass, Amazon Machine Learning, ApacheMXnet, and ImageNet.