> simplified memory architecture (no cache?)

No automatic cache hierarchy. There is local memory, which you manage explicitly. More info here:

https://www.nervanasys.com/nervana-engine-delivers-deep-lear...

Wow, really important details here; very interesting. Now imagine this chip, but shrunk to Intel's latest 14nm process. If they make that chip I have no doubt that it will revolutionize the entire field.

What PeCaN is driving at, is that saying something like you "have no doubt that it will revolutionize the entire field," is simply hyperbole that is not supported. We'll see. Nervana didn't really gain that much traction, which is why it's being acquired. Intel hasn't had much of a deep learning presence so far, which is why they are acquiring.

> If they make that chip I have no doubt that it will revolutionize the entire field.

Ha, ha. Yeah… no. For one thing, there's no mention of its power consumption, no CUDA support, questionable memory design (sure, you can get a million TFlops without cache, now try to get that chip to do anything useful), etc.

Intel probably bought 'em to work on integrated GPUs or Xeon Phi or something.

> Intel probably bought 'em to work on integrated GPUs or Xeon Phi or something.

Ha, ha. Yeah… no.

> there's no mention of its power consumption

Can't be tremendously higher than Pascal for reasons of physics.

> sure, you can get a million TFlops without cache, now try to get that chip to do anything useful

"Without cache" is certainly an exaggeration. It won't have a globally coherent cache hierarchy in the style of CPUs. It certainly will have various on chip memories to hold intermediate results. Neural net workloads are incredibly predictable and homogeneous and are essentially the perfect scenario for hand optimization of data flows to beat automatic caching.

> no CUDA support

You're just being silly now. CUDA isn't a standard, it's proprietary to NVIDIA and this isn't a general purpose processor anyway.

> You're just being silly now. CUDA isn't a standard, it's proprietary to NVIDIA

I would love to agree with you but the reality is that CUDA is the de-facto industry standard e.g. [1].

> and this isn't a general purpose processor anyway.

The "GP" part in GPGPU would like to disagree... [2]

[1] https://en.wikipedia.org/wiki/Comparison_of_deep_learning_so...

[2] http://www.nvidia.com/object/cuda_home_new.html

The GP part stands for "something else than graphics". CUDA is not general purpose in the sense that a lot of types of computation and data movement results in terrible performance. You buy GPU accelerators for performance reasons.

Case in point, GPUs are standard (for now) for training DNNs, but the big server farms that actually do the scoring run on Xeons.

What happened to OpenCL?

https://en.wikipedia.org/wiki/OpenCL

Nothing per se. It's just that it lags far enough behind CUDA that unless you have a specific reason to avoid NVIDIA hardware there are very few pragmatic reasons to use it.

Google has implemented their own deep learning chip with no CUDA support and its serving their purposes well. Why not another industry giant, Intel?

Google is very different from Intel on these fronts at least: 1. Google's open framework TensorFlow is exploding in popularity effectively moving the common interface up from CUDA. 2. Google has a huge need for internal AI/DeepLearning 3. Google has cloud and AI/DeepLearning as a service business 4. Google doesn't sells hardware

However, Intel dominance is not to be underestimated, they definitely can make industry wide impact quickly. Just saying you can not easily draw parallels here

What's stopping them provinding a backend for tensorflow? But yes'm, it's obviously not a given.

> I'm not sure if you're a troll, an overly zealous brogrammer buying into deep learning buzzwords, or retarded, but maybe you should cut it out.

Personal attacks are not OK on Hacker News. Please leave these out of comments.

sieg heil

Google's TPU claims to have a >10x perf/w advantage over GPUs (they state competitors, but GPUs are the defacto standard) and thats frankly more important that raw throughput at scale.

If Intel can ape Nvidia at deep learning then it is a big growth opportunity for them.

They don't state flops because they don't do floats. The nervana chip has some weird fixed point format that they think works better for deep learning. I've heard similar noise out of Google (the other TPU company ...), so I wouldn't be surprised if we'll see the same in many of the specialized chips that people will build for deep learning.

I really don't think CUDA support is important for nervana's offering. They think of themselves as the Apples of deep learning - they want to offer an integrated stack from the chip all the way to the APIs. The way most people use deep learning you don't really need to know CUDA, you just need to use a library that is fast. So it's enough that nervana's engineers know how to write deep learning libs for their own chip. Furthermore I can't see Intel caring that much about CUDA support, since CUDA is owned by Nvidia.

What I've heard about the chip makes it sound really exciting. Many of the trade-offs in deep learning are different from the ones you do in graphics, so specialized hardware makes sense.

Most of the questions asked in these threads on HN are answered in an update article they posted this morning about 14 nanometer, the Neon framework's future and such. Also, looks like everyone who broke the news did got the acquisition amount wrong. http://www.nextplatform.com/2016/08/10/nervana-ceo-intel-acq...