Machine Learning for Systems and Systems for Machine Learning [pdf]
1–10 of 50 posts
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#2This is some really cool stuff, I hope this submission gets more upvotes and reaches a wider audience.
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#3That "Learned Index Structures" makes it pretty clear that Karpathy was right in his widely criticized "Software 2.0" piece.
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#4While this is a collective work, honestly, after hearing about JD for so many years: is there anything he CAN’T do?
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#5That "Learned Index Structures" makes it pretty clear that Karpathy was right in his widely criticized "Software 2.0" piece.
Judging by the downvotes some people really don't like that idea.
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#6[deleted]
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#7Nvidia Titan V can do 110 TFLOPS, 12GB of 1.7 Gb/s Memory [1] and sells for 3,000$. TPU v2 does 180 TFLOPS, 64GB of 19.2Gb/s Memory [2].
That's a heck of a performance boost for a chip that's likely costing google way less than the nvidia flagship.
[1] http://www.tomshardware.com/news/nvidia-titan-v-110-teraflop...
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#8Nvidia Titan V can do 110 TFLOPS, 12GB of 1.7 Gb/s Memory [1] and sells for 3,000$. TPU v2 does 180 TFLOPS, 64GB of 19.2Gb/s Memory [2]. That's a heck of a performance boost for a chip that's likely costing google way less than the nvidia flagship. [1] http://www.tomshardware.com/news/nvidia-titan-v-110-teraflop...
It's not clear to me how programmable the tpu is. I'm sure it's great at convolutions and matrix multiplies. Can it do anything else?
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#9Nvidia Titan V can do 110 TFLOPS, 12GB of 1.7 Gb/s Memory [1] and sells for 3,000$. TPU v2 does 180 TFLOPS, 64GB of 19.2Gb/s Memory [2]. That's a heck of a performance boost for a chip that's likely costing google way less than the nvidia flagship. [1] http://www.tomshardware.com/news/nvidia-titan-v-110-teraflop...
It'd be really interesting to know the per-unit math on that.
Designing and taping out a new ASIC isn't cheap.
Presumably Google needs to use a fairly recent process (22nm or better?), which means GlobalFoundaries/TSMC or Samsung (do any of the Chinese native fabs have 22nm yet?). I wonder who us building them?
So many questions...
Re: Machine Learning for Systems and Systems for Machine Learning [pdf]
#10That "Learned Index Structures" makes it pretty clear that Karpathy was right in his widely criticized "Software 2.0" piece.
I think you got the point though. This is going to be HUGE. But why the downvotes? Job security concern?