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By the time any of this is viable commercially, I really wonder where commercial GPUs and accelerators will be. And they're going to have the home team advanta
by varelse 5y ago
By the time any of this is viable commercially, I really wonder where commercial GPUs and accelerators will be.
And they're going to have the home team advantage when that happens. So that means that unless these things are as accessible to tensorflow/pytorch or whatever the heck the framework de jour is at that point (hopefully more like Jax but better), they will get no traction.
Evidence? Every single demonstrably superior CPU architecture that failed to dislodge x86 over the past 50 years. Sure, it's finally happening, but it's also 50 years later.
- kadoban 5y agoBetter CPU arches were never faster, definitely not by much.
- varelse 5y agoGoing to disagree. I ran circles around contemporary x86 back in the early '90s because of specific instructions Intel denied they would ever need in their processor roadmap. But it really didn't matter and that's one of the most important lessons of my career. They did similar goofy thinking with respect to the magic transcendental unit on GPUs so it's not like they ever learned. It's not entirely about clock rate.
- etaioinshrdlu 5y agoLuckily chip companies know this now and all the alternative deep learning accelerators have some level of support for mainstream frameworks. Getting their support upstreamed to the main framework project is another matter, though, as it reaching the quality of implementation as CUDA and CPU...
- jorgBaller 5y agoHint: Look at the performance delta between the last 3 generatons of nvidia gpus, keeping the TDP fixed. Current accelerators have nice tricks like reducing precision, adding more tensor multipliers etc but thats it. ALl they can do afte r these optimisations is scaling down the feature sizes which will eventually be limited by physics in a few generations. Then what? A fundamentally diferent approach is required
- Salgat 5y agoWe've already seen disruptive architectures like Google's Tensor Processing Units. x86 has already been upended for ML, and photonics based processing units will simply be another PCI card you plug into your computer just like a GPU.
- AbrahamParangi 5y agoHow disruptive are TPUs really though? My understanding is that essentially everything is still trained on Nvidia architecture.
- varelse 5y agoIf you design your network on a TPU, you will tend to use operators that work well on a TPU. And in the end you will have a network that works best on the TPU. Lather rinse repeat for any other architecture. You can even make a network that runs best on Graphcore that way, but it won't be fun to do it. You might even get Graphcore to pay top dollar for it though as they both need some good publicity and they have lots of VC left to squander. This also tends to be true of video games where the platform on which they were developed is the best place to play them rather than their many ports.
- mmmBacon 5y agoWe’ve been doing this for years with DSP and networking. So kind of ho-hum from a HW perspective. If you ask me the thing that makes these things even remotely interesting is the willingness from the SW side to support new HW architectures. Without that you can’t have any innovation in HW.
- singularity2001 5y agoThat's only because google is stupidly refusing to sell their devices
- htkibar 5y agoNot to mention it is only happening due to the emergence of mobile with a different architecture (new field, existing moat is meaningless etc etc)