Running and profiling Arm NN on the HiKey 960

MNIST Draw

MNIST demo application

The MNIST demo application uses a TensorFlow neural network that is trained for MNIST. The demo application also uses Arm NN for inference on Arm Cortex-A or Mali. There are two example applications, one with the simple NN and one with a better neural network. Look at the C++ files for each version: mnist_tf_convol.cpp is the better NN, and mnist_tf_simple.cpp is the simple single-layer network. Both applications read a TensorFlow model. Models are stored in the model/ directory in protobuf binary format. The MNIST data is stored in directory data/ in a simple format that is designed for storing vectors. This directory contains the MIST test data and labels. Build the applications using make, as shown here:

$ cd $HOME/Tools-Solutions/ml-tool-examples/mnist-demo
$ make

The make builds both applications.

The purpose of these examples is to demonstrate how to use Arm NN to load and execute TensorFlow models in a C++ application.