31–40 of 58 posts
For a millisecond I thought Intel were doing something new and interesting. Neuromorphic or neuromeristive architectures maybe. Nope it's just some rebranded x86 cores.
They aren't rebranded x86 cores, This is from Nervana systems which was acquired by Intel few months back. More details about Nervana architecture. https://www.nervanasys.com/nervana-engine-delivers-deep-lear...
Hardware battle over AI platforms is heating up. Will be interesting if Intel can catch nvidia, or GPUs in general.
As someone who works in the semiconductor industry, I'm worried that joining an AI hardware company is a risky move that will falter just like General Magic and other hyped hardware startups.
I would love to hear any opinions on how one might invest in this, the no-brainer Nvidia has had a huge run already, although I wouldn't be surprised if it was double from here in a year's time.
If Intel is smart they will invest heavily into library development. Researchers use whatever is fast and right now cuda is fast, not just because nvidia has the best GPUs (it's about even) but because the primitives in cuda are so much faster. Matrix multiplication on the same hardware is like 3x faster in cuda than opencl or competing libraries and using the neutral networking primitives is even faster. Intel needs…
In my opinion NVIDIA needs a moat. CUDA is a good start but they need proprietary data that's hard to create. I thought they should buy Yahoo which would give them a large and unique data set that NVIDIA users could tie into through an API.
Earlier quoted context omitted.
Does NVidia have a solution here that scales down? Clearly they're focused on scaling up, as that's where the money is today. Yes, they have invested heavily in this. Everytime they talk about their "car/auto segment" (which is all the time if you listen to their investor calls) it's mostly about scale-down.
Cars, especially the sort that will need to do self-driving, will have massive batteries in them. I'm talking about Roomba-sized devices where they can't power a full GPU worth of gear.
https://m.f.ix.de/scale/geometry/1280/q50/imgs/18/1/6/4/4/6/...
Hardware battle over AI platforms is heating up. Will be interesting if Intel can catch nvidia, or GPUs in general.
They are just good enough as integrated GPUs for the occasional gamer and that is it.
Earlier quoted context omitted.
Does NVidia have a solution here that scales down? Clearly they're focused on scaling up, as that's where the money is today. Yes, they have invested heavily in this. Everytime they talk about their "car/auto segment" (which is all the time if you listen to their investor calls) it's mostly about scale-down.
Cars, especially the sort that will need to do self-driving, will have massive batteries in them. I'm talking about Roomba-sized devices where they can't power a full GPU worth of gear.
The NVidia PX2 platform[1] - which is what the Telsa self-driving features uses[2] - is available in a 10W config. I presume this isn't the full self-driving mode, but the Jetson TK1 can do full image tracking and recognition in less than 30W (and that is a 2 year old platform).
[1] http://www.nvidia.com/object/drive-px.html
[2] https://blogs.nvidia.com/blog/2016/10/20/tesla-motors-self-d...
[3] http://developer.download.nvidia.com/embedded/jetson/TK1/doc...
Earlier quoted context omitted.
Intel itself was once a risky company to join, as the whole concept of a microprocessor seemed both stupid and crazy at the same time. Their 4004 series calculator controller was a useless toy compared to more serious computer hardware at the time and their 8008 was marginally less toy-like but equally useless for enterprise computing. We're in the 8088 stage of AI right now.
Good point. I have a cognitive bias against startups because I think what they're doing isn't complex, but it is fallacious.
Earlier quoted context omitted.
If Intel is smart they will invest heavily into library development. Researchers use whatever is fast and right now cuda is fast, not just because nvidia has the best GPUs (it's about even) but because the primitives in cuda are so much faster. Matrix multiplication on the same hardware is like 3x faster in cuda than opencl or competing libraries and using the neutral networking primitives is even faster. Intel needs…
No, researchers use whatever is fastest for development and flexible. Otherwise we'd all be using neon.
Earlier quoted context omitted.
Have any other examples of failed hardware? I love reading about these, most of which I get from r/shittykickstarters, but I'd like to have a rich vein of failed SV startups
Thinking Machines and Transmeta come first to my mind. I'm foggy on details around Thinking Machines, but I vividly remember hype and lustrous lies/promises media pushed about Transmeta.
I would love to hear any opinions on how one might invest in this, the no-brainer Nvidia has had a huge run already, although I wouldn't be surprised if it was double from here in a year's time.
If Intel is smart they will invest heavily into library development. Researchers use whatever is fast and right now cuda is fast, not just because nvidia has the best GPUs (it's about even) but because the primitives in cuda are so much faster. Matrix multiplication on the same hardware is like 3x faster in cuda than opencl or competing libraries and using the neutral networking primitives is even faster. Intel needs…
