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Comment by adamnemecek | Hacker News Reader

I have said this many times and way considered a fool. Electricity is so bad compared with light.

Part of my day job is optics design. There are some tradeoffs. One of them is that the structures of IC's are approaching a couple orders of magnitude smaller than the wavelengths of visible light. To illustrate this point, we have to use something other than visible light to make chips. Getting light through smaller structures becomes quite lossy. A way to make up for the size penalty might be to exploit increases in speed, but the structures for making high speed optics (such as femtosecond lasers) remain bulky.

I'm certainly not trying to nay-say it, and continued research is worthwhile. The history has been that when a new thing is announced that will beat silicon, by the time that thing comes to fruition, silicon has caught up through just grinding incremental improvement.

Optics can solve some interconnection issues, since it isn't confined to 2-dimensional (or "2-1/2" dimensional) structures.

If you could hit terahertz clock speeds something with only 100k transistors and hundreds of nanometers or larger feature sizes could kill modern CPUs… provided you could keep it fed with data from fast enough I/O of course.

Why is visible-light wavelength a limitation? Energy requirements?

Just technology. Visible light can use materials that are relatively straightforward: Glass, pure silica, and some other things can be made transparent to visible light. Silicon is friendly as a detector material. Getting materials, detectors, and light sources, to work at shorter and shorter wavelengths is a technological hurdle. Not insurmountable, but hard.

Dude silicon absorbs below 900nm. We are talking about IR here.

Just to clarify, so we're on the same page, silicon is the most common detector material for visible and UV. It absorbs a photon by kicking an electron into the conduction band, which is then swept to an electrical terminal and registered as a voltage or current.

Silicon transmits IR, and can be used for optics and waveguides. So there's two sides to silicon, with the transition in that regime of about 900 nm.

Actually, silicon photodiodes work down to about 1050 or even 1100 nm, but at a rapid loss of sensitivity and temperature stability. On the UV end, the silicon might be perfectly friendly, but the layers of stuff on top of the silicon become problematic. Silicon is also used for higher energy particles, just due to the havoc caused by absorbing an X or Gamma ray.

True. But short wavelengths are inappropriate for these kinds of devices generally. ~1300nm is the sweet spot for silicon photonics.

Yeah, that's actually a hassle for miniaturizing optics because at any given wavelength, you can make optics out of silicon, or detectors, but not both. The IR detector materials all have their tradeoffs, of course.

Loss is a main issue. It’s minimum at particular wavelengths

Why should we care about size? As far as I understand it real estate isn't a limiting cost for data centers

Ultimately because of cost and scalability. Photonic components are analog devices not digital so they do not have a “quantum” size that scales with process node. The size is the same in any node generally. It is possible to make the devices smaller by making them plasmonic but at the expense of loss. Since these are analog signal chains the signal to noise ratio at the end of the lossy device cascade becomes a significant limiting factor.

For scalability, the larger the die size the more it costs. The yield also becomes a problem as the die area increases due to random defects. I’d don’t understand the visible light comment since silicon absorbs below 900nm (visible and NIR).

One issue is that the speed of light is 30 cm per nanosecond. This limits the size of processors that can run at, say, GHz speeds. It's also a limitation of wires, which is one reason why the original Cray supercomputer had such a funky geometry.

When I was in grad school, the nuclear physics lab had racks of cables of different lengths that were conveniently labeled in nanoseconds.

Analog computers seem to be very well fit for running neural networks (that's since the beginning, this is not the fist time normal computers are beaten), but they are a really bad choice for mostly everything else.

In all likelihood, we will find many more usages for optical processors, but I really don't think they will ever be your main CPU.

Analog computers can't easily be mutliplexed and the integration density of memory + other compute is nowhere near as high as current SRAM/DRAM. This might change with memristive crossbars, but that still doesn't solve the structural part of the problem, since most deep learning workloads are nowadays structurally very far from a feed-forward perceptron and dynamic execution etc. are absent from analog approaches.

Modern architectures are becoming simpler, usually just stacks of transformers, and they are good for text, vision, audio, or almost any task without special modifications. That means if they could implement an optical transformer that is 100x more efficient/faster everyone will want to have it.

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