Zephyr is an open-source, real-time operating system (RTOS) optimized for resource-constrained devices, making it ideal for IoT and embedded systems. It supports multiple architectures and has a modular design.
For machine learning (ML) with Zephyr, developers can integrate frameworks like TensorFlow Lite for Microcontrollers (TFLM) or Edge Impulse. These allow small, efficient ML models to run on devices with limited CPU and memory resources.
The i.MX series from NXP features powerful DSP cores that can offload computational workloads from the main CPU.
This project will focus on leveraging Zephyr RTOS to manage ML workloads on these DSPs efficiently. It will require porting or optimizing existing ML frameworks for the DSP, designing APIs for seamless integration, and demonstrating an end-to-end ML pipeline running on Zephyr. Potential deliverables include support for TFLM on the DSP, and a sample application showcasing the implementation.
Expected Outcomes:
Integration of ML inference frameworks (such as TFLM) on NXP DSPs running Zephyr
Sample applications demonstrating ML inference (e.g., speech recognition, anomaly detection)
Documentation and tutorials for deploying ML workloads on NXP DSPs
Submit pull requests to Zephyr’s upstream repository