Intel Discontinues the Intel Developer Forum; IDF17 Cancelled

Which doesn't necessarily mean anything for Intel's future in machine learning. Intel bought an ARM chip maker before, too, and because of internal conflict of interest with the x86 group, it ended-up killing the ARM division.

Will the same happen to other non-x86 groups? Who knows. But we also see that even Intel's previous "accelerator" divisions have moved focus to being "CPU-driven" as well (Xeon Phi), so we have that data point.

I also remember some of the articles from a decade ago that were sure Intel's "might" would help it succeed in the mobile market. I'm not entirely sure if it's as bad for Intel in the machine learning market, because right now there's pretty much only one competitor rather than many.

It's usually much easier to be second in a market than the 5th, as some customers would inevitably get some raw deals from the incumbent, and they'll want an alternative. But if the machine learning market starts becoming entrenched around GPUs, that second player may end-up being AMD, which I think will have decent alternatives to Nvidia's training and inference GPUs in a year or two (mainly for 2019 with 7nm Navi, though).

Cheaper and MUCH slower, Lower Pref / Watts.

Ryzen close the gap and offer better value for money.

Yes, the Naples 32 cores/64 threads powerhouse is what's dangerous to Intel, especially at its performance level. (datacenter is where Intel probably makes most of its profits, and Naples is eating right into that market)

The ASIC processors Google use are for inference, not training. Having efficient inference is great, but training is what uses up most of the compute power.

The battle between GPUs and CPUs in the datacenter is real, but I wouldn't count out regular CPUs yet. Even with GPU-ready frameworks like Tensorflow, Theano, and Pytorch, it still requires a fair amount of domain expertise to get good performance out of GPUs. These frameworks can automatically offload compute kernels to the GPU, but actually getting good performance still requires you to understand the memory model, what data to pin in GPU memory, how to use the limited GPU memory effectively, etc.

The new instruction sets that Intel have been adding (e.g. all of the recent vectorized FMA instructions) go a long way to making Intel CPUs competitive for a lot of this ASIC/GPU stuff out of the box for things like neural networks. Nvidia has been off to a really great start in this area, and they have carved out a very respectable lead, but I think it's premature to say that Intel (or AMD) are having an existential crisis.

People always talk about Moore's Law falling apart, and it seems like people didn't see it coming. I mean, I didn't, I'm just a guy that reads a lot of scifi. But I always assumed whiz-bang mathematicians had already figured out, I dunno, the threshold at which a processor can be nanosized, or like how big a processor could become before the distances became too great that due to the speed of light further computational advancement wasn't possible. I mean, I know nothing about this stuff, like I said, I just read a lot of scifi and have a high opinion of all the smart folk researching this stuff. Would love to learn more.