Yocto Linux BSPのビルド時におけるTIPS（ビルドの効率化、Dockerを使ってビルド等）を紹介します。

i.MX 8M PlusのEVKをベースに、Yocto Linuxのビルド方法をご紹介します。

Yocto Linux BSPは、6.1.55-2.2.0を使用しています。

PyeIQ is written on top of eIQ™ ML Software Development Environment and provides a set of Python classes allowing the user to run Machine Learning applications in a simplified and efficiently way without spending time on cross-compilations, deployments or reading extensive guides.

Now PyeIQ 3.0.x release is announced. This release is based on i.MX Linux BSP 5.4.70_2.3.0 & 5.4.70_2.3.2(8QM, 8M Plus) and can also work on i.MX Linux BSP 5.10.9_1.0.0 & 5.10.35_2.0.0 & 5.10.52_2.1.0. And also, in latest PyeIQ 3.1.0 release, BSP 5.10.72_2.2.0 is also added into supported list.

This article is a simple guide for users. For further questions, please post a comment on eIQ Community or just below this article.

Share your FreeMASTER dashboard designs, get a board!

Calling all FreeMASTER fans!

Are you an experienced FreeMASTER user who has already created custom dashboards, or are you an HTML/Javascript coder who is new to this great tool and keen to share your creative ideas? With all the possibilities that resources such as jqWidgets, Google, PrimeUI, Plotly.js combined with FreeMASTER offer for creating engaging dashboards to control and/or demo your application, we'd love you to share your creations with the NXP Community. We are giving away* 50 NXP evaluation boards (20 each of the i.MX RT1020 and LPC55S28 EVKs, and 10 of the S32K144EVB) as a thank you to those of you who are willing to share your dashboards with our community.

A FOUR-PART WEBINAR SERIES | 60 MINUTES EACH

FreeMASTER, from NXP, is a powerful real-time debugging and data visualization tool that can help you create engaging demo interfaces for your embedded application. Join NXP for this four-part on-demand training series as we’ll provide an overview of the software, it’s features, capabilities, available examples, application use cases and how to easily get started.

Machine Learning at the Edge:

eIQ Software for i.MX Applications Processors

Developing machine learning (ML) applications for embedded devices can be a daunting task. For the traditional embedded developer, the learning curve can be quite steep, as there are numerous decisions that must be made and new jargon that must be learned. Which framework should I choose? Which model best meets the requirements of my application and how do I know when it’s good enough? What “size” microcontroller or application processor do I need? The questions are many and figuring out how to get started can prove to be a challenge.