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AI software development environment targets edge devices:

Click here for larger image NXP eIQ Edge Intelligence environment (Source: NXP Semiconductors)

NXP eIQ Edge Intelligence environment (Source: NXP Semiconductors)



NXP’s eIQ includes tools necessary to structure and optimize cloud-trained ML models. The goal for NXP customers is to run those ML models in “resource-constrained edge devices for a broad range of industrial, IoT, and automotive applications,” explained NXP.

Describing eIQ as “a one-stop foundation for world-class machine-learning applications,” NXP noted that its AI software developments include:
  • data acquisition and curation tools (e.g., vision, voice and audio front end, sensor);
  • model conversion for a wide range of neural net (NN) frameworks and inference engines, such as TensorFlow Lite, Caffe2, CNTK, and Arm NN;
  • support for emerging NN compilers like GLOW and XLA;
  • classical ML algorithms (e.g., Support Vector Machine and random forest)
Furthermore, eIQ includes tools to deploy models for heterogeneous processing — distributing the ML workload across computational blocks such as Cortex A/M cores, DSP, and GPU — on a range of NXP’s embedded processors.

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