Matthijs Hollemans writes technical articles on machine learning on Apple devices, including Core ML, neural networks, and mobile performance.
HN commenters cite his benchmarks, such as MobileNet V2 speed on iPhone, and his Core ML training post as references in on-device ML discussions.
It's not misleading assuming you know the field.
If you don't you might not realize they are comparing ResNet (designed for ultimate performance) vs EffcientNet to show how close it gets in accuracy in the FLOPs budget.
Note that the best accuracy for EffcientNet is roughly 1-2% better than ResNet/ResNeXT/SENet/etc but does have a much better FLOPs budget.
But these other architectures were never optimised for FLOPs. It wasn't event a consideration when designing them.
And EffcientNet is about a (manually designed) technique for scaling neural networks up in accuracy. Only EffcientNet-B0 is designed by AutoML, the others are scaled up. See the paper[1] for complete details.
Like-for-like should be against MobileNet etc. EfficentNet is better, but the comparison is more reasonable in general.
https://machinethink.net/blog/mobile-architectures/ is a really good overview.
[1] https://arxiv.org/pdf/1905.11946.pdf
You can do some refinement right now already (from WWDC 2019).
But yes, most models are trained on NVIDIA GPUs that are deep learning. All of the other model types can already be trained on regular CPUs.
https://machinethink.net/blog/coreml-training-part1/
Is it?
The edge TPU can do MobileNet V2 at 100 FPS.
An iPhone 7 can do it at 145 FPS (source https://machinethink.net/blog/mobilenet-v2/)
The deal is though that the Edge TPU is able to do it at much lower power.