In recognition of the potential of Zephyr to become the leading independent platform for small IoT devices, Percepio has joined the Zephyr Project as a Silver level sponsoring member.
Two weeks ago we released version 4.5 of Percepio Tracealyzer with a lot of new features in it. Now you can watch Percepio FAE Kristoffer Martinsson as he presents the new features in this video.
In this blog post, we will see how Tracealyzer can be used to quickly and efficiently evaluate multiple implementations of an algorithm in Python, a language that is becoming more common in embedded application development as most machine learning frameworks are implemented in Python.
In this post, we’re going to understand how the combination of LTTng and Tracealyzer can shine light on how compiler options impact performance. The method discussed can come in handy whenever we are evaluating the performance of multiple candidate implementations of a particular feature.
Read the new Percepio Application Note PA-033 to learn how to leverage the ITM support using a Lauterbach TRACE32/µTrace debugger for visual trace diagnostics and analysis in Tracealyzer.
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