NXP eIQ

Machine Learning Software Development Environment.

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Overview

NXP eIQ is a comprehensive toolkit designed to help developers implement machine learning on NXP's broad portfolio of MCUs and application processors. It is not a single tool, but rather a collection of software, libraries, and tools that includes inference engines (e.g., TensorFlow Lite, Arm NN), ML model conversion tools, and optimized libraries. eIQ enables developers to take trained ML models and deploy them efficiently on resource-constrained NXP hardware.

✨ Key Features

  • Support for various ML inference engines
  • Model conversion and optimization tools
  • Integration with common ML frameworks like TensorFlow and PyTorch
  • Optimized for NXP's hardware accelerators (NPUs)
  • Includes example applications and getting-started guides
  • Part of NXP's MCUXpresso SDK

🎯 Key Differentiators

  • Deep integration and optimization for NXP's specific hardware
  • Comprehensive package including multiple inference engines
  • Provided and supported directly by the silicon vendor

Unique Value: Provides a streamlined and optimized path for deploying machine learning models onto NXP hardware, reducing development time and maximizing performance.

🎯 Use Cases (4)

Implementing keyword spotting on microcontrollers Object detection for smart cameras Predictive maintenance using sensor fusion and ML Voice and gesture control for user interfaces

✅ Best For

  • Local voice control on smart home appliances using NXP MCUs
  • Person detection in security cameras powered by NXP processors

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Cloud-based ML model training
  • Development on non-NXP hardware

🏆 Alternatives

STMicroelectronics STM32Cube.AI Edge Impulse TensorFlow Lite for Microcontrollers

Unlike a generic framework like TensorFlow Lite, eIQ provides specific optimizations and examples for NXP hardware. Compared to a platform like Edge Impulse, it is a lower-level toolkit that offers more control but requires more expertise.

💻 Platforms

Desktop (for development) Embedded (for deployment)

✅ Offline Mode Available

🔌 Integrations

TensorFlow PyTorch ONNX MCUXpresso IDE Yocto Project

🛟 Support Options

  • ✓ Email Support

💰 Pricing

Contact for pricing
Free Tier Available

Free tier: Free to use with NXP hardware.

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