Elevate Your ML Journey

Developers want to get the tools and hardware up and running quickly so they can evaluate and learn the new SiMa.ai edge ML platform. The Developer Kit provides an Out-of-Box set of all components that you need and a guide to make this a breeze and let developers focus on their evaluation.

The first step an ML developer will want to do is establish the ML model performance and accuracy on a target platform and assess the capabilities, time and effort in compiling models of interest. Palette™ provides quantization and compilation of a developer’s model using Palette’s compiler. Utilize Palette’s silicon software image build and deploy tools to program the developer board. The developer kit provides the ability to execute these builds and provide KPIs such as; frames per second (fps), latency, accuracy, % loading of compute resources and memory footprint. Palette, running in a Docker on a desktop, supports ARM cross compilation with libraries to generate code for the SoC, a platform build, test and deploy tool suite to create complete ML applications.

The next step an ML developer will want to do is integrate this model into a potential application or use case, including ML model pre and post processing functions. To accelerate the prototyping phase, Palette adds the ability to quickly code, build and evaluate a pipeline using your own ML models. Developers can Python scripting using SiMa APIs for the functional pipeline, avoiding the complex embedded optimization often needed for on-device execution. These APIs bind your Python code to the device execution environment and will execute on device as a complete functioning pipeline. Pushbutton build and deploy loads this image into the Developer board for execution. This proof-of-concept application can validate the functionality of the pipeline as well as provide an early demonstration vehicle.

Quickly bring a real-time data stream to the platform, execute a pipeline on this data stream and display the performance results in real-time running on the MLSoC silicon for use case demonstrations. GStreamer example pipelines included in the Palette software release can be run out of the box to demonstrate real-time streaming performance. Customer pipelines can leverage these pipeline designs that take advantage of GStreamer to get higher utilization of compute resources and higher fps. The developer board processes the pipelines and displays the metadata overlaid on the host PC. Add additional cameras to provide multi-camera processing capabilities.