Raspberry Pi makes it easy and cost-effective to get into embedded machine learning!
The inferencing performance of Raspberry Pi 4, for example, is at par with or even exceeds that of some recently released accelerator hardware in the market but the total hardware cost is significantly lower.
However, using the Raspberry Pi to train custom models is still tricky, just the same with other edge platforms. This makes the recent Edge Impulse announcement a huge step in simplifying the accessibility to machine learning.
Best of all, the full Raspberry Pi support means you are now able to extract data then train against it through the Edge Impulse cloud platform before deploying back the now trained model into the Raspberry Pi.
4 New Raspberry Pi Software Development Kits from Edge Impulse
Edge impulse announced the availability of four new Raspberry Pi SDKs or software development kits which include Python, Go, Node.js, and C++. These SDKs will allow developers to write their custom inferencing applications. Object detection has been added as well. This means Raspberry Pi users can stop relying on previously logged classification models and instead train object detection algorithms using data from the camera connected to the Raspberry Pi.
Edge Impulse users are now able to choose which processor class to use in applications intended for embedded machine learning. Leveraging Edge Impulse’s current best-in-class support if you’re using low-power MCUs is now possible as well as venturing in processor classes running embedded Linux for the highest performance possible.
Edge Impulse is also bringing in the already familiar user experience to Linux developers on Pi 4 with full hardware acceleration. It comes with a new set of capabilities and tools, making the deployment on Linux of embedded machine learning models much easier.
Real object detection has also been launched as part of Edge Impulse’s ML pipeline computer vision. You can now use either any USB interface web camera by plugging it into an available USB port on your Raspberry Pi or the proprietary Raspberry Pi camera. By doing this, you can maximize higher performance computer’s raw power and more complex libraries and frameworks to facilitate applications intended for computer vision.