@Nobody Important thats none of your business

@Nobody Important Time travel.

@Nobody Important The video may have been on private before?

@@outerspaceisalie Indeed. Should we take him out? He might have learned too much...

you have single handled staffed all US semi conductor fabs with engineers in the next 10 years just by posting. THE SAME THOUGHT OCCURED TO ME. BY PRODUCING THESE VIDEOS HE IS OPENING UP A WORLD OF OPPORTUNITIES FOR OTHERS TO SEE AND CONSIDER.

This video is sponsored by chipactVPN

I’m getting my comp eng degree rn rn

definitely, one of the topics I've always been interested in but never had any good source of information about, if I was younger I'd strongly consider partaking in it

It always comes down to logistics and thermodynamics, humanity's two biggest nemeses, doesn't it?

It was not always so in the past. Leakage could also be a major contributor. But it was always known that the wires and power density would become the main problem, both in power and delay.

I wouldn't say electrical signals travel at "essentially the speed of light." That applies to maybe radio waves in free space. But velocity factor/wave propagation speed is typically ~64% the speed of light (Cat 5 data cables) to ~90% the speed of light (RF signals). Without taking insulation into account which reduces VF further. Even if the jump is from 90% to 99% the speed of light, that optimization would result in huge improvement. But like you said, it's about ability to distinguish signals, the accuracy of detection at the receiving end. Without that it's unusable.

@@davidb5205 True, but overcomplicated, so that's why there was a word "essentially" - because it's still by multiple orders of magnitude faster than charge carries move. Also, in photonics light is travelling significantly slower than c too, because it does so in a medium (glass, or whatever), which slows down electromagnetic wave propagation.

If gate transition time is the bottleneck, shouldn't FPGAs or static circuits (simple wires) not have this limitation? Or you can not implement matrix multiplication without using gates?

@@XCSme FPGAs aren't simple wires, they too use transistors and switch state each clock iteration based on input. Static circuits... well, as long as they do not use any capacitors and do not have too much capacitance or inductance on their own, they'd be lightning fast, but as useful as a plain wire or resistor, unable to compute anything. One could say that transistor is a teeny-tiny capacitors. And transistors takes time to charge. The less capacitance they have - the faster is charging. And despite modern nanometers transistors have neglectable capacitance, they still have it and they need some time to charge or discharge. If you have, tons of transistors, but they're mostly in parallel, then you can raise clock cycle maybe to tens of GHz and it would be fine, but you won't be able to do many operations per cycle, only the basic ones. If, on the other hand, you have same number of transistors interconnected with each other like in CPU, then your frequency would be limited by the latest one in chain - you need to be sure that each one in series before this one had enough time to charge/discharge, otherwise last one could end up in incorrect state.

@@aberroa1955 Thanks a lot for the response! I realized that my initial comment was a bit stupid to suggest an FPGA without logical gates, as that's the G stands for in the acronym... That being said, is it really no way to compute anything without using transistors? What if you use the voltage value as the output? Let's say you have to make an addition, if you feed 0.5V and 0.3V at the input, and link them in series, you should get 0.8V (maybe this is just an analog computer? en.wikipedia.org/wiki/Analog_computer ). Also, for example division could be done by adding resistors in parallel, so let's say you want to divide by 3, you feed 1V at the entrance, and then have 3 resistors/paths, to get input divide by 3 you could measure the output voltage of one of the paths.

What types of tools do you develop?

He said at the end a 1D row of interferometers can perform like a 2D array using time instead, would the same priinciple apply for 2D to 3D if the accuracy for 2D can be improved ?

@@raphaelcardoso7927 We develop tools that leverage Artificial Neural Networks to simulate the performance of photonic devices. All of our softwares are free and open source.

@@Soken50 I am not sure, since I do not deal with the theory-sort of stuff, my wokr us mostly in the software development side of things.

Hi can we connect on LinkedIn ??

thanks to you, he really made light work of this topic!

Thank you Alex, all the best blessings to you and yours.🌟🌟🌟🌟🌟🌟🌟🌟

This.

@@tf_d I just noticed a mistake in my comment. I said that the datapath on a 40ghz core would be 7.5mm. In reality datapaths are normaly pipelined so, each individual stage of the pipeline would be limited to that length (this totaly applies to electronic computers). Pipelines on cpus today are around 8-20 stages long. Im not sure if pipelining would work on photonics and I think there would need to be electronic circuits between the stages anyways.

@@billwhoever2830 I don't see why pipelining wouldn't be possible with photonics, they're technically able to do anything that an electronic circuit can.

@@rufushawkins3950 Very simplified it means something is quantifiable, as in a photons energy is discrete in a way.

@@rufushawkins3950 When used as a noun it means small, when used as an adjective it means big. English!

Geometry is the key to solving the quantum mechanics problem

Wow, there is so much wrong with this one small comment that it would take an essay to pick it apart.

How does this interference from the environment behave? Can it be algorithmically modeled? If so, I believe it might possible to create a noise generator which mimics this behavior during neural network training. This can help “robustify” the neural networks, to prepare them for inference on such optical devices. This can provide an software rather than hardware approach to mitigating the accuracy issue.

It's Speed of Light Through a Medium... Switching to photonics removes the need for conductive metals and voltage transformation. Light TX/RX is a lot more simple, and a much clearer medium so the speed of light is faster in that medium.

Small correction (I may be nitpicking) electricity can transmit data at speeds of 50%-99% speed of light

1. A 1xn matrix is a vector so eh... plus if you do batch learning you end up with true matrix-matrix products

yeah, but I still feel like mentioning matrix-matrix multiplication is going to confuse the average viewer more than illuminate, compared to matrix-vector. most ML accelerators are built to accelerate matrix-vector products (e.g.: they use weight stationary systolic arrays). this is because accelerators rarely have the memory bandwidth to support matrix-matrix products at full throughput; they require the higher operational intensity of the static matrix/dynamic vector product.

2. 28 orders of magnitude is a lot

Petajoules in a chip sounds fun

Convolutions are typically expressed using im2col, which makes them an instance of the matrix-matrix multiply. They are extremely common in vision-based applications, so I think the statement is absolutely justified!? I would consider the questions whether a matrix-matrix product is decomposed into matrix-vector multiplications in a given accelerator an implementation detail, rather than an inherent feature of the underlying problem.

Happy to know that i am not only one following her

Oy another Anastasia followers nice

I wonder if you got the photonics cheap enough and small enough, the accuracy could be improved by running the same calculation multiple times and averaging it. Though the extra electronics and redundancy might offset any gains made…

Can you give a better set of keywords or a full title?

@@SianaGearz why pure information gives of heat By up and atom

@@stefanklaus6441 Watched it yesterday, great video

gates are used to make "bits" interact and potentially effect a change of states, that change will of course necessitate a certain amount of work, however tiny it is we can't get a system to change states without expending energy somewhere.

Also, the problem is how DL model is querried in reinforcement learning scenario - it is querried thousands of times per step for simulating the game in it's "state space" (evaluation of a tree of future steps)

Katago, which is a Go AI based on AlphaGo Zero with some extra improvements, is superhuman at just a couple hundred playouts, which on my computer (gtx 1650) only takes a couple seconds to achieve (about 3-5). On a high end computer this is achieved in less than a second per move. The original AlphaGo was a frankenstein of neural networks and needed a lot of MCTS rollouts to make up for it, subsequent Go AIs can be superhuman running on an iphone even

LEDs aren't sensitive enough

@@animeshthakur5693 Sure, but in theory, it is possible to integrate LED within semiconductor die process.

Yes, but you probably wouldn't want to, for this to work you want coherent light, which for LEDs is going to mean throwing away most of it. A laser is what you want here really

@@itonylee1 if I recall, integrating the light source well is actually one of the major pitfalls/ cost centres that is as of yet unresolved. Integrating with the design means you don't need to align it/tune it. But making light sources out of silicon is really hard.

And today we have Meta's chatbot dissing Zuck! 🤣

the bit-flip that caused that move really broke Kasparov

Electrons do flow in low frequency conductors, especially the DC power. The energy is indeed in the fields, the motion of charge carriers are described by the edge of the fields (current and magnitude)

What is the point of using a neural network as an ALU

I don't think anyone is interested in training on photonic accelerators, it's all inference. Quantization is very commonly employed to make inference cheaper, which results in errors similar to photonic accelerators, though smaller in magnitude (IIRC current photonic accelerator designs get 2-4 bits of precision, classical inference accelerators are typically in the 8-16 bit range). So I think most of what you're saying here is a non sequitur with regard to the published research.

@@taktoa1 Run something as simple as MNIST on an optical accelerator and get 99% accuracy and then we'll talk. The key with digital quantized neural networks is that despite the fact they're quantized they are also deterministic, that is, given an input, the output is the same each time, as there is no measurement noise. Therefore if you train with quantization error, the network can learn that error. However, analog physical systems have measurement error. It's not just that the optical system achieves the "equivalent" of 2-4 bits of precision, its that no matter how many average photons are used to represent a signal, there are going to be measurement outliers. Due to the nonlinear operations of ReLu and Maxpool, outliers due to measurement error can accumulate in deep neural network layers. So it seems to me that having many deep layers and nonlinear operations like ReLu and Maxpool make it extremely difficult for an analog multiplier, especially one susceptible to quantum noise, is going to produce reproducible, reliable inference. Because of the extreme sensitivity of feedforward neural networks to cumulative error, if training is performed digitally for inference that is to occur on an analog/optical computer, the training model must be extremely accurate, including effects of quantization, noise sources including Poisson, thermal, coherent noise, system manufacturing error, etc., and even then the variation due to measurement error may limit the ultimate inference accuracy. It may be required to train a neural network for each physical system because the manufacturing tolerances of two different optical chips may be too different for a network trained on one chip to work on another chip. Biological neural networks seem to work quite effectively without being deterministic despite the fact these are implemented on analog computer wetware. Deep feedforward neural networks seem like a poor fit for analog computing, especially quantum noise limited computing for which the power consumption is directly influenced by the number of photons required to achieve a certain SNR due to Poisson noise (SNR being proportional directly to the square root of power, and so SNR increasing only slowly with increased power consumption). Even other solutions that use electric charge (mythic.ai/) with similar electric charge quantization problems are limited in the number of layers that can be implemented. The whole reason why feedforward deep neural networks were created in the first place is because backpropagation is possible using a bit of clever calculus and the chain rule. Training is the problem, because if you don't have any other kind of neural network you can effectively train that is resistant to measurement error, analog computation is not going to be a viable solution for neural network inference. Neural network accelerators like the Tensor processor have sucked all of the air out of the room for research into any other kind of neural network architecture, and as long as this is the case, the market will not care about analog computers because the current feedforward deep neural networks were created for deterministic, digital machines.

I worked on this datacom side for photonic switches. The thing about wavelength multiplexing (WDM) when used with MZIs is that crosstalk can be a killer, depending on the MZIs used. Depending on the interconnect topology used for the MZI mesh, crosstalk can cascade through the MZI mesh ultimately increasing the "noise" level beyond practicality. This also inhibits scaling these meshes out, as you could imagine!

Usually the nonlinearity is achieved outside of the photonic part :/

Check out Xanadu Photonics, squeezed state photons make quantum computing also possible in photonics - although the photodetectors have to be cooled in a liquid oxygen bath.