Multi-Stream YOLOv8 Object Detection with Ara240 DNPU on i.MX This post shows a walkthrough of the ARA2 Vision Examples package and its multi-stream YOLOv8 object detection application. The ara2-vision-examples package provides vision AI examples for NXP i.MX platforms using Ara240 DNPU acceleration. It demonstrates real-time video processing with AI/ML inference capabilities such as object detection, classification, pose estimation, and semantic segmentation. This walkthrough focuses on the multistream_yolov8 application, which uses GStreamer to process up to eight simultaneous video streams, run YOLOv8 object detection on each stream, and display the results in a single mosaic view. Supported Platforms FRDM i.MX 8M Plus FRDM i.MX 95 Key Features Multi-stream video processing from 1 to 8 streams YOLOv8 object detection accelerated by Ara240 DNPU Support for YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x models GStreamer-based video pipeline Mosaic display output with bounding boxes Runtime options for stream count, model selection, synchronization, and endpoint selection FPS and IPS performance overlay per stream Running the Demo Run the application with the default settings: multistream_yolov8 Run with a specific number of streams: multistream_yolov8 -s 4 Select a different YOLOv8 model: multistream_yolov8 -s 4 --model yolov8s Run eight streams for maximum throughput: multistream_yolov8 -s 8 --sync false Enable synchronized playback: multistream_yolov8 -s 4 --sync true Walkthrough Video   In the attached video, it is shown how to launch the application, configure the number of streams, select different YOLOv8 models, and view the object detection results in the mosaic display. Links ARA2 Vision Examples repository: https://github.com/nxp-imx-support/ara2-vision-examples Multi-stream YOLOv8 README: https://github.com/nxp-imx-support/ara2-vision-examples/blob/main/tasks/object-detection/yolov8n/multistream-gstreamer/README.md

The Runtime SDK for AI/ML acceleration using the Ara240 NPU on NXP i.MX SoCs, provides the runtime environment for the Ara‑2 NPU.   Main Purpose of the Package    1. Provide the runtime environment for the Ara‑2 NPU This runtime sdk package installs everything needed for an i.MX system to communicate with and utilize the Ara240 NPU hardware, including: NPU drivers  Low‑level utilities (metrics, hardware bring‑up, flash tools) Proxy services that interface applications with the NPU Firmware loaders and NPU configuration files 2. Allow users to run AI/ML inference models on the NPU The Ara240 runtime environment includes tools for: Downloading pre‑compiled AI/ML models Running performance tests on the NPU Running classification, detection, pose, and segmentation models Inspecting HW IPS (inference/second) and real hardware performance   Scripts such as: fetch_models.sh ara_metrics.sh chip_info.sh program_flash.sh run_models_perf.sh 3. Automatically configure and optimize the i.MX system Installation does the following automatically: Expands system partition to handle large models (LLMs/VLMs) Sets up an 8GB SWAP for devices with limited RAM Prepares the runtime environment for AI workloads 4. Manage and update Ara‑2 firmware The package contains scripts to: Check the installed firmware version ( chip_info.sh ) Update firmware if needed ( program_flash.sh ) 5. Provide systemd service for automatic startup The SDK installs: A systemd service: rt‑sdk‑ara2.service   Walkthrough Video Below is the walkthrough video for this package