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Path-breaking Papers About Image Classification

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Re: Path-breaking Papers About Image Classification

#31
post #28

Earlier quoted context omitted.

Moore’s law is still alive and well, you just have to move over to the parallel architectures like GPU's. Considering machine learning is all on GPU's and TPU's now, I think this is still a fair assessment.

You're conflating things here; Moore's law is about how many transistors you can pack onto a chip, not performance. Moore's law is unsustainable due to laws of physics. Transistors are reaching size scales where quantum effects like tunneling dominate. Think of a transistor as having two regions, separated by a channel. When the transistor is on, charge carriers flow through the channel between the regions. When off,…

Todays transistors are still generally on a plane. If we could find ways to build IC's stacked thousands or millions of layers thick, we could really start using the third dimension.

("3d" transistors built on their side don't really count)

Re: Path-breaking Papers About Image Classification

#32
post #28

Earlier quoted context omitted.

You're conflating things here; Moore's law is about how many transistors you can pack onto a chip, not performance. Moore's law is unsustainable due to laws of physics. Transistors are reaching size scales where quantum effects like tunneling dominate. Think of a transistor as having two regions, separated by a channel. When the transistor is on, charge carriers flow through the channel between the regions. When off,…

Todays transistors are still generally on a plane. If we could find ways to build IC's stacked thousands or millions of layers thick, we could really start using the third dimension. ("3d" transistors built on their side don't really count)

For CPUs/GPUs it probably wouldn't help that much, since you have to get rid of the heat produced by switching a transistor.

And for storage the main issue isn't how big it is, but how cheap it is to manufacture.

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