eIQ^™ for Glow Neural Network Compiler

The eIQ machine learning (ML) software development environment for i.MX RT crossover MCUs supports the Glow machine learning compiler, which enables ahead-of-time compilation. The compiler converts the neural networks into object files, then the user converts this into a binary image for increased performance and smaller memory footprint as compared to a traditional runtime inference engine.

Glow is used as a software back-end for the PyTorch machine learning framework, including support for the ONNX model format.

Glow, or graph lowering, compiler derives its name because it lowers a neural network into a two-phase strongly typed intermediate representation. In the first phase, the optimizer performs domain-specific optimizations. The second phase allows the compiler to perform optimizations that take advantage of specialized back-end hardware features. It’s in this second phase that NXP has added specialized support for Arm^® Cortex^®-M cores and Cadence^® Tensilica^® HiFi 4 DSP support, accelerating performance by utilizing Arm CMSIS-NN and HiFi NN libraries, respectively.