The eIQ Machine Learning (ML) Software Development Environment for the Glow Neural Network Compiler
eIQ Machine Learning (ML) software development environment leverages inference engines, neural network compilers, optimized libraries, deep learning toolkits and open-source technologies for easier, more secure system-level application development and ML algorithm enablement, and auto-quality ML enablement.
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.