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.
phyCORE-i.MX7 Software Release (BSP-Yocto-FSL-iMX7-PD18.2.1)
Software Release Name: BSP-Yocto-FSL-iMX7-PD18.2.1
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.
Embedded World 2019 – Demos
It’s trade show season! Every year PHYTEC America joins our German counterparts from our headquarters in Mainz to attend Embedded World. This year is no different and PHYTEC America is contributing with a few exciting demos.