[shipp02]

IIRC, Nvidia was already ahead in software support 5 years ago. Image recognition was the big thing and the only way I could train them was on Nvidia GPU or Google tpu.

With this context, the articles clain that

>Intel correctly identified AI as the future

seems tenuous at best.

It also claims that Nvidia was just a graphics company in 2019-20. This was clearly not the case for anyone training cv models at the time.

IDK what Gaudi is or what Habana Labs was developing. Does anyone know what the status of pytorch/tensorflow on Intel GPU hardware is? Because add far as research is concerned one of the framework has to be supported.

[buildbot]

>Intel correctly identified AI as the future

I'd say this is true - they just failed to execute meaningfully on it. They were adding stuff like VNNI back in 2018, buying Nervana and Habana, but then just...didn't do much with it. At the same time, they made Ponte Vecchio.

Software support for all of these is not great, in my personal opinion.

For the Ponte Vecchio and Xe GPUs, pytorch 2.5 allegedly supports it now: https://pytorch.org/blog/intel-gpu-support-pytorch-2-5/

For habana, I think they have a custom interface that hooks into Pytorch: https://docs.habana.ai/en/latest/PyTorch/Reference/Python_Pa...

There's a random Intel 1100 Max with 48gb HBM on eBay, if anyone is inclined to try it...https://www.ebay.com/itm/387631533950

[seanmcdirmid]

Nvidia GPUs have been used to train DNNs since around 2011 (the paper on how to do it was written in 2010 I think?). Nvidia was also used heavily to mine crypto currency until recently.

[buildbot]

2012? If we are using Alexnet as the "starting" mark for DNN training on GPUs. Though probably there were efforts before that, Alexnet is the most well known for sure.

[seanmcdirmid]

> As the speed of GPUs increased rapidly, it was soon possible to train deep networks such as convolutional networks without the help of pretraining as demonstrated by Ciresan and colleagues in 2011 and 2012 who won character recognition, traffic sign, and medical imaging competitions with their convolutional network architecture. Krizhevsky, Sutskever, and Hinton used a similar architecture in 2012 that also features rectified linear activation functions and dropout for regularization. They received outstanding results in the ILSVRC-2012 ImageNet competition, which marked the abandonment of feature engineering and the adoption of feature learning in the form of deep learning. Google, Facebook, and Microsoft noticed this trend and made major acquisitions of deep learning startups and research teams between 2012 and 2014. From here, research in deep learning accelerated rapidly.

…

> AlexNet is a convolutional network architecture named after Alex Krizhevsky, who along with Ilya Sutskever under the supervision of Geoffrey Hinton applied this architecture to the ILSVRC-2012 competition that featured the ImageNet dataset. They improved the convolutional network architecture developed by Ciresan and colleagues, which won multiple international competitions in 2011 and 2012 by using rectified linear units for enhanced speed and dropout for improved generalization. Their results stood in stark contrast to feature engineering methods, which immediately created a great rift between deep learning and feature engineering methods for computer vision. From here it was apparent that deep learning would take over computer vision and that other methods would not be able to catch up. AlexNet heralded the mainstream usage and the hype of deep learning.

https://developer.nvidia.com/blog/deep-learning-nutshell-his...