Profiling AlexNet on Raspberry Pi and HiKey 960 with the Arm Compute LibraryOverview Set up your Raspberry Pi NFS on Pi Build the Arm Compute Library on Pi Run the graph_alexnet application on Pi Start Streamline gatord on Pi Add Streamline annotations and rebuild on Pi Build the Arm Compute Library on HiKey 960 Profile with Streamline on HiKey 960 Next steps
As we've seen, the Arm Compute Library can be profiled with Arm Streamline to study the performance of Machine Learning and Computer Vision applications.
This guide demonstrates how you can use Streamline to profile an example application from the Arm Compute Library for the AlexNet Convolutional Neural Network on two different hardware platforms with different operating systems.
Going forward, you can apply the methods shown here to use Streamline to profile your own machine learning applications to help you optimize their performance for running on Arm-based systems. You can use Streamline to report further information such as memory used, disk I/O, threads created, and sample based function profiling. Furthermore, the Arm Compute Library makes use of the available Neon hardware to perform efficient image inference.