Getting started with AI on NXP i.MX8M Plus/Introduction/Overview/i.MX8M Plus hardware specifications

Getting started with AI on NXP i.MX8M Plus
Introduction
Overview i.MX8M Plus hardware specifications Development tools Development kit DART-MX8M-PLUS Development kit Quick getting started Yocto minimal image Flashing SD card First steps on the board
Development
Gatesgarth and Hardknott Yocto versions Downloading Yocto images for NXP based BSP Building an imx-core-image Flashing SD card Integrating multimedia software stack Installing GStreamer Installing OpenCV Integrating Artificial Intelligence software stack Installing TensorFlow Lite for NXP based images Installing R2Inference Installing GstInference Developing software for the board Building the SDK Crosscompiling apps for GStreamer, TensorFlow Lite, and OpenCV Exploring TensorFlow Lite delegates for prototyping
Neural Processing Unit
Introduction Profiling the NPU Installing gputop Setting the board on debug mode Use Case experiments: Smart Parking Introduction to the use case License Plate Detection TinyYOLO version 3 Training a model compatible with GstInference License Plate Recognition Rosetta Getting the model prototyping Bash scripts for CPU usage and time estimation Serial experiments Parallel Experiments Results
GStreamer
Exploring Rosetta element for character recognition Example pipelines Smart Parking Video inferences Image inferences Video streaming
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The board used in this entire research is the Variscite DART-MX8M-PLUS based on NXP i.MX8M Plus, and some of the main characteristics are listed in the table below.

Hardware module	Specification
CPU	- 4 cores ARM Cortex A53. - Frequency: [1200, 1800] Hz. - Supported 32 and 64 bits. - One thread per core.
GPU	- 3D GPU GC7000UltraLite. - 2D GPU GC520L. - Uses Winograd Algorithm.
NPU	- Reaches up to 2.3 TOP/s. - Optimized for voice and image recognition, object detection Uses Winograd Algorithm.
RAM	- 4 GB. - LPDDR4-4000.

Note: For more information please visit the Official Variscite site.