YouSaid · the spoken record

Jeffrey Shainline

lines on the record: 208
first: 2021-09-26
most recent: 2021-09-26
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Doing anything It has to work at large scale in order to overcome that power penalty. But that's possible. It's just it's going to have to get that performance. And the answer is I don't know. I think if it's just overall better than silicon at a problem that a lot of people care about, maybe it's image classification, maybe it's facial recognition, maybe it's monitoring credit transactions. I don't know, then I think it will have a place. It's not going to be in your cell phone, but it could be in your data center.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, yes. You can use for the Yeah, one of the things that Mike Schneider is working on right now is an image classifier at a relatively small scale. I think he's targeting that nine pixel problem where you can have three different characters and you just you put in a nine pixel image and you classify it as one of these three categories. And that's going to be really interesting to see what happens there because If you can show that even at that scale you just put these images in and you get it out and you can, he thinks he can do it. I forgot if it's a nanosecond or some extremely fast classification time. It's probably less. It's probably 100 picoseconds or something. There you have challenges though because the Josephson junctions themselves, the electronic circuit is extremely power efficient. Some orders of magnitude for something more than a transistor doing the same thing. But when you have to cool it down to four Kelvin, you pay a huge overhead just for keeping it cold, even if it's not.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, at all scales, right? I mean, so sometimes I'm more drawn to. The underlying phenomena, the critical dynamics of this system, trying to understand how elements that you build into your hardware result in emergent, fascinating activity that was very difficult to predict, things like that. But I got to be really careful because I think a lot of other people who, if they found themselves working on this project in my shoes, they would say, all right, what are all the different ways we can use this for machine learning actually let me let me just definitely mention a colleague at NIST, Mike Schneider. He's also very much interested, particularly in the superconducting side of things, using the incredible speed, power efficiency, also Ken Segal at Colgate, other people working on specifically the superconducting side of this for machine learning and deep feed forward neural networks. There, the advantages are...”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm really hesitant to over promise, so I really don't know. Also, I don't really understand machine learning in a lot of senses. I mean, Machine learning from my perspective appears to require that you know precisely what your input is and also what your goal is. You usually have some objective function or something like that. That's just very limiting. I mean, of course, a lot of times that's the case, you know, there's a picture and there's a horse in it, so you're done, but that's not a very interesting problem. I think when I think about intelligence, it's almost defined by the ability to handle problems where you don't know what your inputs are going to be and you don't even necessarily know what you're trying to accomplish. I mean, I'm not sure what I'm trying to accomplish in this world.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely not. If you're interested in short term making money, go with deep learning, use silicon microelectronics. If you want to understand Things like the physics of a fascinating system, or if you want to understand something more along the lines of the physical limits of what can be achieved, then I think single photon communication, superconducting electronics is extremely exciting.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“For me, the goal is to study this as a scientific, physical system. I'm not drawn towards turning this into an enterprise at this point.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's at different levels, and we've got this simple spice circuit stuff. That's no problem. And now we're building these network models based on this more efficient leaky integrator. So we can actually reduce every element to one differential equation. And we can also step through it on a much coarser time grid. So it ends up being something like a factor of a thousand to ten thousand speed improvement, which allows us to simulate, but hopefully up to millions of neurons. Whereas before we would have been limited to Tens, a hundred, something like that”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Can be used to achieve lots of different types of dynamical activity. And to me, that's where scalability comes from and also complexity as well. Complexity is often characterized by relatively simple building blocks connected in potentially simple or sometimes complicated ways and then emergent new behavior that was hard to predict from those simple simple elements. And that's exactly what we're working with here. So it's a very exciting platform, both from a modeling perspective and from a hardware manifestation perspective where we can hopefully start to have this test bed where we can explore things not just related to neuroscience, but also related to other things. that connect to other physics like critical phenomenon, icing models, things like that. So you were asking how we simulate these circuits.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Results in a lot of differential equations that need to be solved simultaneously. We were looking for a way to simulate these circuits that is scalable up to networks of millions or so neurons is sort of where we're targeting right now. So we were able to analyze the behavior of these circuits. And as I said, it's based on these simple building blocks. So you really only need to understand this one building block. And if you get a good model of that, boom, it tiles and you can change the parameters in there to get different behaviors and stuff, but it's all based on now it's one differential equation that you need to solve. So one differential equation for every synapse, dendrite, or neuron in your system and for the neuroscientists out there, it's just a simple leaky integrated fire model leaky integrator, basically. A synapse is a leaky integrator, a dendrite is a leaky integrator. So I'm really fascinated by how this one simple component”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You can, but that becomes computationally expensive. So, one of the things when COVID hit, we knew we had to turn some attention to more. Things you can do at home in your basement or whatever. One of them was computational modeling. So we started working on adapting, abstracting out the circuit performance so that you don't have to explicitly solve the circuit equations, which for Joseph's injunctions usually needs to be done on like a picosecond.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a great question. There's a lot of different scales of design. So at the level of just one synapse, you can use conventional methods. They're not that complicated as far as superconducting electronics goes. It's just for Joseph's injunctions or something like that, depending on how much complexity you want to add. So you can just directly simulate each component in spice. Standard electrical simulation software. So you're just explicitly solving the differential equations that describe the circuit elements.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Since all of the computation is happening in these flux storage loops and they play such a central role in how the information is processed, how memories are formed, all that stuff. I didn't think too much about it. I just called them loop neurons because it rolls off the tongue a little bit better than superconducting optoelectronic neurons.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, the term loop neurons comes from the fact, like we've been talking about, that they rely heavily on these superconducting loops. So even in a lot of forms of digital computing with superconductors, storing a signal in a superconducting loop is a primary technique. In this particular case, it just loops everywhere you look. So the strength of a synaptic weight is going to be set by the amount of current circulating in a loop that is coupled to the synapse. So memory is implemented as current circulating in a superconducting loop. The coupling between, say, a synapse and a dendrite or a synapse and the neuron cell body occurs through loop coupling through transformer. So current circulating in a synapse is going to induce current in a different loop, a receiving loop in the neuron cell body.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, absolutely. Once you're at this scale, to me, it's just obvious. Of course, you're using Light for Communication. You have fiber optics given to us, you know, from nature so simple. The thought of even trying to do any kind of electrical communication just doesn't make sense to me. I'm not saying it's wrong. I don't know, but that's where I'm coming from.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And it has to be fractal all the way. You're exactly right because that's the only way that you can efficiently get information from a small point to across that whole network. It has to have the power law connectivity.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Then you're in business. Now you can get millions of neurons on a wafer, but that's not anywhere close to the brain scale in order to get to the scale of the human brain. You're going to have to also use the third dimension in the sense that entire wafers need to be stacked on top of each other with fiber optic communication between them and we need to be able to fill a space the size of this table with stacked wafers and that's when you can get to some 10 billion neurons like your human brain. And I don't think that's specific to the optoelectronic approach that we're taking. I think that applies to any hardware where you're trying to reach commensurate scale and complexity is the human brain.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You absolutely have to be using the third spatial dimension. And that means on the wafer, you need multiple layers of both active and passive components. Active, I mean superconducting electronic circuits that are performing computations. And passive, I mean these waveguides that are routing the optical signals to different places. You have to be able to stack those. If you can get to something like 10 planes of each of those or maybe not even 10, maybe five, six, something like that.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Electrons for communication, you have these wires where, okay, the size of an electron might be angstroms, but the size of a wire is not angstroms. And if you try and make it narrower, the resistance just goes up. So you don't actually win to communicate over long distances. You need your wires to be microns wide. And it's the same thing for waveguides. Waveguides are essentially limited by the wavelength of light, and that's going to be about a micron. So whereas compare that to an axon, the analogous component in the brain, which is 10 nanometers in diameter, something like that, they're bigger when they need to communicate over long distances. But grappling with the size of these structures is inevitable and crucial. And so in order to make systems of comparable scale to the human brain, by scale here, I mean number of interconnected neurons.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, so yes, you were talking about what are some of the technical limitations. One of the things that I believe we have to grapple with is that our brains are miraculously compact. For the number of neurons that are in our brain, it sure does fit in a small volume, as it would have to if we're going to be biological organisms that are resource limited and things like that. Any kind of hardware neuron is almost certainly going to be much bigger than that if it is of comparable complexity, even whether it's based on silicon transistors. Okay, a transistor, seven nanometers, that doesn't mean a semiconductor-based neuron is seven nanometers. They're big. They require many transistors, different other things like capacitors and things that store charge. They end up being on the order of 100 microns by 100 microns, and it's difficult to get them down any smaller than that. The same is true for superconducting neurons. And the same is true if we're trying to use light for communication. Even if you're using”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Voltage to drive current through that now semiconducting part. So that light source is the semiconducting part of a neuron. And so the neuron has reached threshold. It produces a pulsive light That perform this process themselves. So it's probably worth explaining what a network of wave guides is because a lot of listeners aren't going to know that. Look up the papers by Jeff Chiles on this one. But basically light can be guided in a simple basically wire of usually an insulating material, so silicon silicon nitride different kinds of glass, just like in a fiber optic it's glass, silicon dioxide. That makes it a little bit big. We want to bring these down so we use different materials like silicon nitride. But basically just imagine a rectangle of some material that just goes and branches, forms different branch points that target different sub-regions of the network. You can transition between layers of these. So now we're talking about building in the third dimension, which is absolutely crucial. So that's what we've got.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's all electrical. It's all electrical in the super kinetic domain for anybody who's up on their superconducting circuits. It's just based on a DC squid, the most ubiquitous, which is a circuit composed of two Joseph's injunctions. So it's a very bread and butter kind of thing. And then the only place where you go beyond that is the neuron cell body itself. It's receiving all these electrical inputs from the synapses or dendrites or however you've structured that particular unique neuron. And when it reaches its threshold, which occurs by driving a Joseph's injunction above its critical current, it produces a pulse of current which starts an amplification sequence, voltage amplification that produces light out of a transmitter. So one of our colleagues, Adam McConn and Sonia Buckley as well, did a lot of work on the light sources and the amplifiers that drive the current and produce sufficient”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Close, you're not at fault for thinking that that's what I meant. What I what I should say is that if you wanted to be a synapse, you tack a detector, a superconducting detector onto the front of it. And if you wanted to be anything else, there's no optical component.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's determined by putting some resistor in that superconducting loop. So a synapse event occurs when a photon strikes a detector, adds current to that loop, it decays over time. That's the postsynaptic signal. Then you can process that in a dendritic tree. Bryce Primavera and I have a paper that we've submitted about that for the more neuroscience oriented people. There's a lot of dendritic processing, a lot of plasticity mechanisms you can implement with essentially exactly the same circuits. You have this one simple building block circuit that you can use for a synapse, for a dendrite, for the neurons. For all the plasticity functions, it's all based on the same building block, just tweaking a couple parameters.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You're using superconductors for this, the energy of that circulating current is less than the energy of that photon. So your energy budget is not destroyed by doing this analog computation. So now in the language of a neuroscientist, you would say that's your postsynaptic signal. You have this current being stored in a loop. You can decide what you want to do with it. Most likely you're going to have it decay exponentially. Every single synapse is going to have some given time constant.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“When a photon hits that superconducting single photon detector, current is added to a superconducting loop. And the amount of current that you add is an analog value. It can have 8 bit equivalent resolution, something like that, 10 bits maybe.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so the synaptic weight will tell you how many fluxons you pop into the loop. It's an analog number. We're doing analog computation now.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Right. So let me say big picture. Based on optics, photonics for communication, superconducting electronics for computation, how does this all work? A neuron in this hardware platform can be thought of as circuits that are based on Joseph's injunctions, like we talked about before, where every time a photon comes in, so let's start by talking about a synapse. A synapse receives a photon one or more from a different neuron, and it converts that optical signal to an electrical signal. The amount of current that that adds to a loop is controlled by the synaptic weight. So as I said before, you're popping fluxons into a loop, right? So a photon comes in, it hits a superconducting single photon detector, one photon, the absolute physical minimum that you can communicate from one place to another with light.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, yeah. So let me take a moment here because I haven't really described what I mean by a neuron or a network in this particular hardware.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And when you want to answer the scientific question about what are the physical limits of cognition, well, the physical limits, they don't care if you're at 4 Kelvin. If you can perform cognition at a scale orders of magnitude beyond any room temperature technology, but you got to get cold to do it, you're going to do it. And to me, that's the interesting application space. It's not even an application space. That's the interesting scientific paradigm. So I personally am not going to let low temperature stop me from realizing a technological domain or realm that is Achieving in most ways everything else that I'm looking for in my hardware.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It is the showstopper for a lot of people. And understandably, I'm not saying that That's not a consideration. Of course, it is. For some, okay, so different motivations for different people. In the academic world, suppose you spent your whole life learning about silicon microelectronic circuits, you send a design to a foundry, they send you back a chip, and you go test it at your tabletop. And now I'm saying here now learn how to use all these cryogenics so you can do that at four Kelvin. No, come on, man. I want to do that. That sounds bad.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And yet it's not stopping people from investing in that area. And by investing, I mean putting their research into it as well as venture capital or whatever.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Want to say about temperature is that If you can swallow that, if you can say, all right, I give up applications that have to do with my cell phone and the convenience of, you know, a laptop on a train and you instead, for me, I'm very much in the scientific headspace. I'm not looking at products. I'm not looking at what this will be useful to sell to consumers. Instead, I'm thinking about scientific questions. Well, it's just not that bad to have to work at 4 Kelvin. We do it all the time. Usually has to work at something like 100 millivin, 50 milli Kelvin. So now you're talking of another factor of 100 even colder than that, a fraction of a degree. And everybody seems to think quantum computing is going to take over the world. It's so much more expensive to have to get that extra factor of 10 or whatever colder.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Don't have to live at the edge of the universe. The aliens that are more advanced than us in their solar system are doing this in their asteroid belt. We can get to that.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It's already colder than 4K in the expanses, you know, you don't have to get that far away from the Earth in order to drop down to not far from 4K.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Siberia culture. Okay, so just for reference, the temperature of the cosmic microwave background is about 2.7 Kelvin. So we're still warmer than deep space”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Let me just dwell on that for Kelvin for a second because some people hear four Kelvin and they just get up and leave. They just say, I don't, I'm not doing it, you know. And to me, that's very earth-centric, species-centric. We live in 300 Kelvin, so we want our technologies to operate there too. I totally get it.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So now you don't need good light sources. You can have the world's worst light sources as long as they spit out maybe a few thousand photons every time a neuron fires you have the heart, you have the hardware principles in place that you might be able to perform this optoelectronic integration. To me, optoelectronic integration is just so enticing. We want to be able to leverage electronics for computation, light for communication, working with silicon microelectronics at room temperature that has been exceedingly difficult. And I hope that when we move to the superconducting domain target at different application space that is neuromorphic instead of digital and use superconducting detectors, maybe optoelectronic integration comes to us.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“In that conversation, I failed to mention that semiconductors can also receive photons. That's the primary mechanism by which it's done. A camera in your phone that's receptive to visible light is receiving photons. It's based on silicon or you can make it in different semiconductors for different wavelengths. But it requires on the order of a thousand, a few thousand photons to receive a pulse. Now, when you're using a superconducting detector, you need one photon, exactly one. I mean, one or more. So, the fact that your synapses can now be based on superconducting detectors instead of semiconducting detectors brings the light levels that are required down by some three orders of magnitude.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, one other thing about the light source is I said that silicon is terrible at emitting photons. It's just not what it's meant to do. However, the game is different when you're at low temperature. If you're working with superconductors, you have to be at low temperature because they don't work otherwise. When you're at four Kelvin, silicon is not obviously a terrible light source. It's still not as efficient as compound semiconductors, but it might be good enough for this application. The final thing that I'll mention about that is, again, leveraging superconductors, as I said in a different context, superconducting detectors can receive one single photon.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So actually, let me say one other thing about the light sources. And then I'll move on, I promise, because this is probably tedious for some.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, the computation is done in the superconducting electronics, and the light sources receive signals that say, hey, a neuron reach threshold, produce a pulse of light, send it out to all your downstream synaptic connections. Those are, again, superconducting superconducting electronics. Perform your computation, and you're off to the races, your network works.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The prospects for integrating light sources with that kind of an electronic process are certainly less explored, but I think much more promising because you don't need those light sources to be intimately integrated with the transistors. That's where the problems come up. They don't need to be lattice matched to the silicon, all that kind of stuff. Instead, it seems possible that you can take those compound semiconductor light sources. Stick them on the silken wafer and then grow your superconducting electronics on the top of that. It's at least not obviously going to fail.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“One is that, as I mentioned, it's difficult to integrate those compound semiconductor light sources with silicon. With silicon is a requirement that is introduced by the fact that you're using semiconducting electronics. In superconducting electronics, you're still going to start with a silicon wafer, but it's just the bread for your sandwich in a lot of ways. You're not using that silicon in precisely the same way for the electronics. You're now depositing superconducting materials on top of that.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“All right, so let me now after spending 45 minutes trashing light source integration with silicon, let me now say why I'm basing my entire life. Professional life on integrating light sources with electronics. I think the game is completely different when you're talking about superconducting electronics. For several reasons, let me try to go through them”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think so, yes. And again, I need to go back and make sure that I'm not taken the wrong way. I'm not saying that the pursuit of integrating compound semiconductors with silicon is fruitless and shouldn't be pursued. It should. And people are doing great work. Kaime Lao and John Bauer's others, they're They're doing it and they're making. Just the standard monolithic light source on silicon process. I just don't see it.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And the silicon chip drives the light source chip and modulates the intensity of the light. So you can get data out of the package on an optical fiber. And that still gives you tremendous advantages in bandwidth as opposed to sending those signals out over electrical lines. somewhat peculiar to my eye that they have to be integrated at this package level. And those people, I mean, they're so smart. Those are my colleagues that I respect a great deal. So it's very clear that it's not just They're making a bad choice. This is what physics is telling us it just wouldn't make any sense to try to stick them together.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So now you have some, I would say a great deal of architectural limitations that are introduced by that sort of package level integration as opposed to monolithic on the same chip integration, but it's still a very useful thing to do. And that's where I had done some work previously before I came to NIST. There's a project led by Vladimir Stoyanovich that now spun out into a company called IR Labs led by Mark Wade and Chen Sun where they're doing exactly that. So you have your light source chip, your silicon chip, whatever it may be doing, maybe it's digital electronics, maybe it's some other control purpose, something.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So let me just try and be clear. You can use light for communication in digital systems. Just the light sources are not intimately integrated with the silicon. You manufacture all the silicon, you have your microchip, plunk it down, and then you manufacture your light sources, separate chip, completely different process, made in a different foundry. And then you put those together at the package level.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“However, I say it's physics, not engineering, because it's very difficult to get those compound semiconductor light sources situated with your silicon in order to do that ion implantation that I talked about at the beginning. High temperatures are required. So you got to make all of your transistors first and then put the compound semiconductors on top of there. You can't grow them afterwards because that requires high temperature. It screws up all your transistors. You try and stick them on there. They don't have the same lattice constant, the spacing between atoms is different enough that it just doesn't work. So nature does not seem to be telling us that, hey, go ahead and combine light sources with your digital switches for conventional digital computing.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Compound semiconductors like we talked about back at the beginning, an element from group three and an element from group five form an alloy where every other lattice site switches which element it is. Those have much better properties for generating light. You put electrons in, light comes out. Almost 100% of the electron hold, it can be made efficient.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source