Practical comparison with discrete GPUs: AMD Radeon Pro 560 in MacBook Pro 15, and nVidia Titan RTX in a Windows PC

Introduction

There are enormous articles showing benchmarks of Apple’s M1 SoC. The results indicate its potential not only as a CPU but also as a GPU and a neural processor.

An M1 MacBook Air was delivered to my desk in the second week of December 2020. Before saying welcome to this newcomer, I’d like to see its performance by myself using my research work, semantic segmentation of computed tomography images.

Machines and Methods

I compared its performance with the following machines:

  • MacBook Pro 15 inch with AMD Radeon Pro 560
  • Hewlett-Packard workstation with nVidia Titan RTX

MacBook Pro 15 inch is a product in…


Spring is a beginning for all the creatures. Sakura in a local shrine in Ikeda city, Osaka / All the photo credits — Takashi Shirakawa

Photos of Japanese culture and scenes in seasons


Sample code for the Core ML computation

Backgrounds

I use a Windows machine with nVidia’s powerful GPU for training my Keras model with a huge dataset. This means that I must convert the trained Keras model into Apple’s Core ML format when I use the model for prediction in iPhone, iPad, and Macs.

Additionally, my Keras model has custom layers, my original layers, for better performance.

After a long and winding road, I had successfully imported my custom layers of Keras into my Core ML app. I’d like to share my experience with programmers because there is little information showing the way from the beginning to the end.

Takashi Shirakawa

Surgeon + Engineer + Programmer / Love eating, outdoor and travel

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