Hello everyone,
I'm currently working on an AI computer setup powered by NXP's eIQ Machine Learning Software on the i.MX 8M Plus processor. The goal is to leverage machine learning capabilities for real-time data processing, and I'm seeking recommendations on how to best optimize the integration for maximum efficiency.
Here are the main areas I am focusing on:
- Model Optimization: I'm using TensorFlow Lite models, and I would appreciate any insights into optimizing these models for the eIQ software stack. Any tips on reducing inference times or improving performance would be very helpful.
- Hardware Acceleration: The i.MX 8M Plus processor offers several features that could enhance performance for AI applications. How can I best utilize hardware acceleration (like the NPU) for tasks like image and video analysis in an AI computer setup?
- Edge AI Integration: I'm also exploring the integration of Edge AI solutions with eIQ software. If anyone has experience deploying Edge Impulse workflows with eIQ, it would be great to hear your thoughts on best practices and any relevant resources or tutorials.
I believe NXP's eIQ software offers strong capabilities for AI deployment, especially in edge computing applications, making it an excellent choice for optimizing AI computer performance.
I look forward to hearing your experiences and any advice on making the most of this powerful combination for AI computer solutions.
Thanks in advance for your help!
