Angelo Dragone (SLAC): Looks like there's only one person here.
Angelo Dragone (SLAC): Hello?
Angelo Dragone (SLAC): Hey, Gregor.
gdeptuch: Hey, hey, and…
Angelo Dragone (SLAC): It's just the two of us.
gdeptuch: So far! So far!
Angelo Dragone (SLAC): So far. Oh, it's early. Okay, I'm 3 minutes early.
gdeptuch: Two minutes, or 2 minutes, probably.
Angelo Dragone (SLAC): Okay, okay, okay. I thought, I thought for a second that there was something not… not… not working. I'm in the wrong meeting. He said, no, it's me, so I'm early.
gdeptuch: No, you… but… but there was few…
gdeptuch: meetings, I mean, the Zoom addresses, you know, views, so frankly, I don't know if we're 100% on the right one, because it was one for SAPI, and then it was another for, you know, kind of…
gdeptuch: general meeting of everyone, so we have to be sure, because if there are only two of us on this one, maybe there are three. Hello!
gdeptuch: Good ocean.
gdeptuch: So probably we are in the right one.
Oceane Bel (PNNL): Oh, I just clicked on the one that came out with the notification, so I hope it's the right one.
Angelo Dragone (SLAC): I have only this one on my calendar, so must be.
gdeptuch: Okay, so we have to wait for others to join us.
Oceane Bel (PNNL): Today's ELFO presentation, woo!
gdeptuch: Supposed to be! Let's see.
Oceane Bel (PNNL): Come on, positive, right?
gdeptuch: Yeah, you know, if nobody else is joining, we will have a private screening for Anteloch.
Oceane Bel (PNNL): There you go.
Angelo Dragone (SLAC): Well, let me see if that's.
gdeptuch: We are in… we are dangerously close to… It's at 6pm.
gdeptuch: But Angela, I haven't seen any email from Paul, effectively…
Angelo Dragone (SLAC): Then I'll see an email from Paul. I see Jim, okay, at least that is Jim.
gdeptuch: This is our team, right? This is our team, at least. So I'm not surprised they were connected to this meeting, but I haven't seen any email from either…
gdeptuch: Paul, or…
gdeptuch: Michelle, I think, right?
Angelo Dragone (SLAC): Well, I can always at least send a reminder to the outage people. That's easy enough.
gdeptuch: It would be strange, frankly, because…
gdeptuch: last meeting, or no, before the Code D presentation, we're clearly saying that the schedule is 15 with the Code D, and 22nd is helpful. Oh, there are people joining, okay.
gdeptuch: Now he's the… now everybody's booking up.
James (Jim) Ang: Yep, and there's Paul.
jsnelso: Okay.
gdeptuch: So, when we have auto, we have Quorum.
Paul McIntyre: Okay, looks good. People joining?
Angelo Dragone (SLAC): And a reminder for our race, we'll see.
'Pops' / NY CREATES: Hey, Pops. Hello. Sorry, I haven't been able to join earlier, but
'Pops' / NY CREATES: I keep… I keep missing it. Hi, Jim.
Angelo Dragone (SLAC): Why… why we wait for other people to join? Maybe a question.
Angelo Dragone (SLAC): Are we gonna have our,
Angelo Dragone (SLAC): nearcut, annual meeting, because, I know that, the other,
Angelo Dragone (SLAC): gather up, have, have, have started doing that. For instance,
Angelo Dragone (SLAC): China at their first annual meeting.
Paul McIntyre: We, we had talked about it, I think at.
Angelo Dragone (SLAC): I was not… sorry.
Paul McIntyre: Yeah, at least one of our, one of our prior meetings, but we, we didn't… I don't think we quite had a quorum of, of the, of the PI team present for that. We, we talked about,
Paul McIntyre: we didn't really get into timing. We talked a little bit about format, and I think there was some, some consensus, or some, you know, belief expressed that it might make sense to do this
Paul McIntyre: with a subset of PIs, rather than the whole PI team, because it would… that might make it a little unwieldy.
Paul McIntyre: And also, we might want to do it in, proximity to, to Germantown, to, make it easier for
Paul McIntyre: the DOE staff to attend. If, if that's the objective of the review, is to have some interaction with them, because they often find it difficult to travel.
Paul McIntyre: But of course, with the government shutdown.
Paul McIntyre: you know, we… I think we'd probably need to wait
Paul McIntyre: In any case, until that's over, for public fac… for public-facing or quasi-public-facing events.
Paul McIntyre: or events that require a lot of expenditure of government funds to host, that the timing might not be right for that while there's a shutdown. At least that would be my concern, I guess. But that doesn't mean we can't start planning it now.
gdeptuch: But also, we were talking about having pretty much clear directives, maybe, from our program managers, what they would be expecting from us when reporting, because there is no really reason to have such
gdeptuch: meeting without knowing what to report, and having, you know, people from DOE. I mean, this was also mentioned last time that we…
gdeptuch: we… I think someone was supposed to figure it… figure it out. Oh, maybe Valerie was supposed to look at that.
gdeptuch: We, we…
Valerie Taylor: What, what was, what was I supposed to…
gdeptuch: But what DOE program managers are expecting, frankly, from us, is reporting materials when we plan our annual meeting for Meerkat.
Valerie Taylor: Oh, that part. So I, you know, I did talk to Pavel when he… because he was in the area and stopped by, Argonne.
Valerie Taylor: So,
Valerie Taylor: it's unclear when they're going to request, a report from us, and so, you know, and I was telling him that we're just moving forward with our collaboration plan as to what was described in the plan, and he was like, great.
Valerie Taylor: And that was all we said, and then he moved on to the American Science Cloud.
Valerie Taylor: So we had, you know, we did give them an update on what we're doing with BEA, but in terms of, you know, looking also,
Valerie Taylor: At the centers in general, yeah, that was just, like, a 2- or 3-minute conversation.
Paul McIntyre: Yep.
Paul McIntyre: Yeah, I think it's difficult to get their attention. I mean, even with the government shutdown business, they were very… I mean, we've been waiting for quite a while to get… to receive feedback on our original collaboration plan.
Paul McIntyre: in ESTEAM, we've been collecting up papers and… and trying, you know, trying to get things, sort of ready so that we're… we… we… when… if a report request comes, we won't… it won't be that difficult for us to respond to it. But,
Paul McIntyre: Yeah, we ought to think about having, you know, having a plan for a, for a review, or a, at least an, at least for an annual meeting, and, how we want to structure that.
Paul McIntyre: Maybe we can make that the topic of our next PI meeting, is just trying to work through that.
Valerie Taylor: Right, and what about the working groups? Because I think we were all looking at names for the working groups, so that we can at least move forward with the four working groups.
Valerie Taylor: have forward movement there. Such that… and I think what we had discussed previously was to have
Valerie Taylor: One person from each project serve on an initial working group team, and that way, that team can lay out a plan for that working group, maybe over the next 18 months.
Valerie Taylor: And then, using that plan, we get others engaged, and they can use that plan as a starting point, but it's much easier to work with 8 people than 30 people.
Valerie Taylor: When the… when the page is blank. So…
Paul McIntyre: Yeah, so let's, let's have these as our two agenda items for our next, meeting, next, next week. And, as, as you know, we're going to launch a new, meeting, series, with a different, Zoom link.
Paul McIntyre: And and that… you'll… that will be coming in the next day or so.
Valerie Taylor: Okay, and so that Zoom meeting, that's where we have the PIs and also a deputy or alternate…
Valerie Taylor: So, it's two people per project.
Paul McIntyre: That's right.
Valerie Taylor: Okay.
Paul McIntyre: Yeah.
Valerie Taylor: Yes.
Paul McIntyre: Sounds good.
gdeptuch: Okay, so maybe, maybe we switch to our L4, because we are… we are working… we're waiting to start.
Paul McIntyre: Yes, I know, and we want to make sure you have enough time, Gregor, so, please, so the purpose of this meeting is to see, to have an overview of the ELFO team and what their plans are.
gdeptuch: Yes, so hopefully, hopefully you can see my screen.
gdeptuch: Can you see my screen? Yep.
Valerie Taylor: Looks good.
gdeptuch: Yes. So, we'll be switching between,
gdeptuch: tasks during the presentation, but I will start with introduction and covering Trust 1, and then my colleagues will also be covering Trust 2, Trust 3, and maybe let's start with that. So, the outline of the presentation is, first part gonna be about project organization.
gdeptuch: Some words of introduction, motivation, discussing electrophotonic interfaces and silicon photonic platforms that were label polarization of the project. Then, at high level, project objectives and trust will be covered.
gdeptuch: And then project design, stack, and tools, because we are actually covering several layers of, of approaches from,
gdeptuch: materials up to the system level, and then we covered three trusts of, within ELFO, Trust 1, Trust 2, Trust 3, then a bit testing… testing investment in equipment, because it's essential for this project. So I don't have to remind you how this project actually was generated, because we have… we are part of the… of the Meerkat Center. The Meerkat Center, we have
gdeptuch: projects, from the…
gdeptuch: In ELFO, we have collaboration from… collaborators from Brookhaven, Columbia University, CUNY, CCNY, and FIU, Florida International University, and PNNL. We have a great industrial liaison, New York Create, represented here by COPS, and also we have an industrial collaborator with Cadence.
gdeptuch: So you can look a bit, some details at our websites that are under construction, of course, one about ELFO, and another one
gdeptuch: about Newcastle Center. Generally speaking, the second one is under construction. We are actually now getting some help from a company to get it, you know, quicker up to the level that could be good for our center.
gdeptuch: So, maybe let's jump to motivation, because whenever you have any commercialization, you know, training, you're always being told you should address customer needs, and our, you know, working on developments of highly granular detectors.
gdeptuch: for the electron-ion collider that is a flagship project at Brookhaven, we realized that we are suffering in such great-scale systems from congestion in delivering resources and taking data.
gdeptuch: So this… what is shown here in red is all that congestion of wires, cables, delivering power, taking signals out, and basically, some areas have already more… more material than is expected, and this object is very, very complex. We are using large-scale silicon detectors that are bent.
gdeptuch: to make this… make them thin, so the level of complexities that are present here is gigantic. And particularly in the center of the detector, we have so-called monolithic active pixel sensor, silicon vertex tracker, that is made of large-scale detectors as one big part of the silicone wafer. This is a 12-inch wafer, and one sensor is effectively marked
gdeptuch: as this red rectangle, and here there are multiple units producing a lot of data, and they are, from one such long tail, we have six 10 gigabits per second drivers. Okay, so we started scratching our heads how to actually handle all that.
gdeptuch: And the first place we were thinking about jumping into
gdeptuch: photonics, very close to the endpoint of such long slab of monolithic sensor. We are thinking about delivering power through light, and also taking data using photonics approach. So we are thinking about designing a photonic IC that will be, you know, taking the 6-bit… 6
gdeptuch: times 10 gigabit per second link out of the detector. So we realized that there are existing good commercially mature processes where you can do silicon photonics, already. So this was actually our starting point, thinking about the L4 project, or L4 scope.
gdeptuch: And then we realized that, effectively, when you start doing wavelength division multiplexing that is very advantageous for transferring data, you can actually obtain very high
gdeptuch: speeds of transmission, but using multiple links. Here, the plot shows, for example, this is a borrow from our collaborators from Colombia, that if we go with multiple wavelengths, which they typically are multiplex of single photonic fiber, we can actually get speeds that are very, very high. At the same time, where we can save a lot of energy.
gdeptuch: So this is actually what we somehow previously, already we tried to actually address by…
gdeptuch: multiple streams of 10 gigabits per second on… using different wavelengths. And that wavelength division multiplexing will be appearing through our presentation several times, because this is underlying technology for also terraforming infotron computing. We'll be talking later.
gdeptuch: Okay, later… now when you look at the photonic system, how they are constructed nowadays, when you have… you have typically photonic photonic elements sitting a bit on the side of
gdeptuch: of electronic circuits, when you have pluggable optics, fibers for data transmission. But if we think about embedded photonics, which is, you know, subject of our project, we can… we bring the photonics inside the hybrids, having, you know, all the elements ranging from memory, processors, drivers, sensing, all on one stack.
gdeptuch: So…
gdeptuch: knowing that we have some experience already in the past in hybrid 3D integration, I mean, for example, you know, we've been involved in development of 3D stacked, fully fusion-bonded detector that was composed of the sensor, analog layer, digital layer. We started already having, you know, oh, we have something in our pocket that we have a
gdeptuch: hybridization in 3D stacking. Also, when we have, our collaborators working, working on high-speed, multiplexing on data using waiver and division multiplexed principle, we can have processing, added, by having electropical modulators, and then in the, in the
gdeptuch: the flow of data before even reaching the final destination when data is stuck. Okay, so we realized we have components that are available in our hands, so we thought that Alpha project can actually have such a structure. We may have a generic detector that is instead
gdeptuch: reading read out electrically can be read out photonically, but having conversion to the photonics as close as possible to the signal generation part, then we… when we transfer photonic data out, we can perform already significant level of processing, on-the-flight processing in the photonic domain, using advantages of addition of light, and also using the wavelength multiplex, as I said earlier.
gdeptuch: But later, towards the end, we can also equip such a data acquisition system in electrophotonic… high data throughput processing, where you can have neural networks, non-phenolmon processing embedded, and this is why we trust… we created three trusts.
gdeptuch: Trust one is electrophotonic detector, Trust 2 is electro… is on-the-flight data processing, and trust three is electrophotonic processors, and those are the three thrusts that we have in our…
gdeptuch: alpha project. So Trust 1 focuses on devices and interfaces, system characterization and modeling, application-driven device optimization. Trust two is on-the-fly data processing with a focus on enables and interconnects, partitioning of data, because certain functions which will be discussed later, can be optimally realized in electrical domain, certainly in photonic domain. And trust three is
gdeptuch: processor operation beyond von Neumar architectures, where we harness advantages of electrical and photonic signals. And these three trusts are tied together with, you know, a co-design approach that is heterogeneous integration, high bandwidth interfaces between Trust 1 and Trust 2, and co-design architectures between Trust 2, Trust
gdeptuch: and trust three, and all of us, we are aiming towards energy efficiency in an extreme environment, as was our funding proposal plan. So, projects, objectives, and trusts, as I mentioned, we have myself, I'm the leader of the L4, we have great industrial liaison, we have project manager, we have Trust One, led by, you know.
gdeptuch: myself and Alex from Colombia, Trust 2, led by Sumi Ajit, who is present in the room, and Karen, who couldn't join us today, unfortunately, and Trust 3, led by Jim and Prashanta, and also we have Ocean connected to us.
gdeptuch: And we identified major technological components, because without that, it's difficult to convey such scale of projects, ranging from material to system. We work with Infotonics, Global Foundries, Tower Selling Conductor, especially the last one is for the sensing part.
gdeptuch: So, we were thinking significantly, you know, from where we should be actually starting, and we realized that existing
gdeptuch: commercially-grade electrophotonic platforms are already equipped with a lot of elements that could be harnessed by us, and we can start constructing the systems. Also, we have having access to more, like, laboratory-scale silicon photonic platforms, and also beyond silicon photonics platform, we can enhance this portfolio.
gdeptuch: And I think from electrical photonic modulation, certainly we have in existing platforms electro-absorption modulators.
gdeptuch: phase interferometric modulators. We have also some much more advanced hybrid electrical modulators. Going back from photonic to electrical, we have direct photodiodes based on… also on Germanium. We have balanced photodiodes, balanced photodetectors, heterodine, coherent detector, all of that is available on the platform.
gdeptuch: Could be used for our needs.
gdeptuch: And then application in data computation, we have high-speed data links, we have models for unomorphic artificial intelligence accelerations, you know, sensors, so all that enables, actually, actually what we realize, closed-loop electrophotonic systems for transmission, computation, and sensing, and this is where we embedded our tree trusts.
gdeptuch: So, the stack… L4, design stack and tools, in L4 project, we are starting with materials and devices, going up, particularly reaching algorithms and applications.
gdeptuch: And at each level of this stack, we need to have different tools, and the tools are essential, because when we're designing systems, we need to have models ranging from high-precision device, going all the way up to high abstraction levels. So here are listed the tools
gdeptuch: And boundaries between trusts are, you know, shared between certain types of the tools. So we have multi-physic device modeling we exploited, mixed electrophotonic circuit simulators, analysis of EM, ER, signal and power integrity, and then on the top, we have processing architectural modeling, functional simulator for
gdeptuch: simulation for the design. So today, tools versus needed tools, because we… certainly the very mature are tools for electrical design, so there are vendors available… making tools available, but photonics in the AI is still fragmented and immature.
gdeptuch: So this is also what we plan to cover a bit in our Alpha to gap, to cover this gap with our approaches that we'll be aiming at development of the tools.
gdeptuch: So, this, maybe, slide is a bit bulky. I will… I will keep going into details, but this can be taken as a reference. So, we have… we put on the left side existing industry-grade tools.
gdeptuch: So this is for stable, you know, this is very stable for silicon photonic… silicon photonic processes, but what is missing is uncharted territory that we are planning to cover in L4s on the right side.
gdeptuch: So I may probably skip it in terms of time to allow, you know, covering more our trusts, but this is, you know, a good compendium of kind of market research, also on this rich portfolio, and sometimes also quite expensive if it goes to commercial tools packages. But when we want to bridge the gap between photonics.
gdeptuch: circuits and electrical circuits, and going all the way up to high level of abstraction, the tools are largely missing, and we will be… we are having this also hung up addressed in our project.
gdeptuch: So, maybe now going to Trust 1, I will still cover Trust One, and then I will give microphone to my colleagues. So, in Trust One, as I mentioned, this…
gdeptuch: Focused on electrophotonically readout, sensors, and the inspiration for this, development is, was effectively,
gdeptuch: It's coming from the fact that in photonic domain, we can realize quite easily linear operations, where we have
gdeptuch: analog values.
gdeptuch: that are represented in amplitude of light, coupled to other waveguides using couplers, resonant circuits, that we can assign different weights to them, and by doing this, we can actually construct XY readout that can be fully going on the photonic domain. So this is, like.
gdeptuch: kind of transfer a bit of knowledge or experience from computing in photonics to developing fully read out in photonic manner detector, like monolithic active pixel sensors. And of course, we have to go through several layers, starting from the front-end layer, which is detector sensor itself.
gdeptuch: Then the conversion layer to… from electrical to photonic domain, data transport, and application. So, having all these elements achieved in Trust 1, we can develop monolithic active pixel sensor with 3D stacked photonic reader, and our idea is shown maybe on this slide more… in more details. So, when we have the first layer physical of sensor and analog front-end
gdeptuch: That is, shown here.
gdeptuch: That, this, layer
gdeptuch: Of course, detect signals, and it feeds electro-optical modulators that are receiving signals from the wave… from waveguides, where we have multiple wavelengths sent over there, and then we have these electro-optical modulators, sensitive to specific wavelengths, and coupling to the output fiber, where we can read already modulated light.
gdeptuch: So if there is signal, for example, in a given channel, we can encode addresses, and we can read at unprecedented speed, uncomparable to whatever is possible to be achieved in the electrical domain. So the cross-sections are also shown here, that's what we plan, so having, you know, of course, 3D stacked approach with sensor.
gdeptuch: Electrical layer and electro-optical layer is added in the stack.
gdeptuch: how we plan to actually realize this, the tasks. So, we can fabricate, of course, sensors or electrical layers on the full-scale wafers, and this is why we have also tower in our equation, that we can develop sensing as a monolithic system, and add photonic circuits using chip on wafer stacking.
gdeptuch: To have photonic layer attached to
gdeptuch: to the electrical layer. Of course, one part of our Trust One is also development of
gdeptuch: very efficient coupling that is suitable for an extreme environment, including cryogenic temperatures, and also another part of this trust one is multi-physics simulation and modeling, because we are not only merging layers as heterogeneous materials, but also
gdeptuch: two different domains, electrical and photonic, so all that needs to be carefully modeled, and we will… it's also solidly planned in Trust… Trust One. So,
gdeptuch: optimization of the process, of the process, and this is where we have advantage of working in laboratory scale, maybe not laboratory scale, not commercial-grade foundry, where we can optimize also basic components, such as resonant microdisk modulators, and here is an example of work performed by Columbia.
gdeptuch: we're optimizing microdis modulators by having some undercut technique. We can increase sensitivity of tuning. This actually is shown by having plot different powers used for, modulation, how wide the spectrum of modulation, wavelength
gdeptuch: resonance we can obtain, so I think this is…
gdeptuch: advantage of working with a founder that is not only hermetically closed for achieving, you know, data transmission, but also on R&D, and this is part of our Trust One efforts.
gdeptuch: And probably with… with this, I will pass,
gdeptuch: battle to Trust 2, and Sumi Ajit will cover Trust 2. Okay, so to continue, so once the data has been generated, so the thrust 2 is, the… well, the focus of Thrust 2 is to, process the data, while it's moving, so moving away from the idea of the interconnect just being a passive interconnect in, like, a wire, to see if it can do some processing.
gdeptuch: That is useful. So, in the… as Irish already mentioned, the… the main, enabler here is wavelength vision multiplexing, because we have this additional degree of freedom in optics because of the huge bandwidth that we can actually pack thousands of, hundreds to thousands of, wavelengths, each modulated individually at pretty high rates, you know, tens of gawbits per second.
gdeptuch: to a single fiber, and this gives us very, very large bandwidths. And the way this typically works is that you have a continuous wave laser that gets modulated by these micro-resonators, these are group velocity dispersion tweaked micro-resonators, you get this curve effect, and that gives you this comb. The comb can then be individually de-interleaved through this tree network, so each of these gives you a splitter, and then the splitter is going to
gdeptuch: geometry. So then once that has been split up, each of these individual wavelengths can then be modulated.
gdeptuch: And then you can recombine them. So now you have a bunch of modulated tones that are on the same fiber. These can be translated over, I mean, transferred over the fiber, single-mode fiber typically, and then de-interlaced again at the other end, detected, and then passed out further processing, which could be done electrically, or could be further
gdeptuch: optical processing if you don't put the photodiodes. So… so this gives you… this is the generic framework that you can use, but then the work done at Columbia, in particular, Karen's group, has really gone, taken this to the next level, to… to look at how efficiently you can actually transfer data using this, and the results together are pretty promising. You can get less than a picojoule per bit, and multi-bits per link.
gdeptuch: terabits per second per link.
gdeptuch: Next slide, Brian. So, in this context, the additional degree of freedom that the wavelength division multiplexing WDM gives you
gdeptuch: is…
gdeptuch: is that you get this additional degree of freedom for processing data, and the one way to look at this is to compare how you might do a very common operation, such as a convolution operation, in a traditional, let's say, digital accelerator, like a GPU, versus an analog in-memory accelerator, which is shown in the middle, versus a photonic tensor cord, which is shown on the right. And here, the tensor really means that now you have this additional degree of freedom in your vector, which means
gdeptuch: another dimension, which you can use to do multiple matrix vector multiplications in one step, as opposed to a traditional approach, or digital, which is sequential, or even in-memory computing, where you do it in one step. But now you can go beyond that and do n of these operations in parallel. So it's massive parallelism.
gdeptuch: Very, very high computational throughput.
gdeptuch: So something that Karen's group has been working on is to take advantage of this, couple this with another key enabling technology here, which is a waven-selective Switch. So, WSS is a waven-selective Switch.
gdeptuch: And this is basically the analog of a router, but in the optical domain. And the key idea here is that it's, actually, you can take any wavelength at one of the input ports and route it to one of the other output ports. And this gives you switching, arbitrary switching inside an optical network. Now, the interesting thing is this is a fully passive,
gdeptuch: passive structure, so, you know, in terms of power consumption, it doesn't add, but then it's also very high, data rates, because, again, you're doing it with fiber. So, compared to an optical… sorry, compared to an electrical router, you get these advantages.
gdeptuch: And, this lets you conceptually now build up a sort of a hierarchical network where you have this, these, modules, which they call B-cubes, which have a computational agreement, the CU. Right next to it is one of these, is a silicon Photonics I.O. with.
gdeptuch: one of these, WSS, multiplexing, several of these cubes together.
gdeptuch: Now, you can take this structure, which is just a one layer, and recursively extend that to hierarchically as many layers as you like, and this lets you build up these very high bandwidth networks with
gdeptuch: optical back… optical switching of the… optical circuit switching of the connections. And this can… I mean, they have demonstrated experimentally and also chosen simulations that this typically gives you something like.
gdeptuch: 10x more, bandwidth per, say, watt, or 10x more energy efficiency for data transmission than an equivalent of a state-of-the-art InfiniBand or other wired electrical network. So this can be very useful if you're doing operations that require a large amount of,
gdeptuch: Data transfer, particularly, for example, if you're doing distributed training of deep neural nets, when you have operations like, broadcast, you have to go out across the internet, so…
gdeptuch: The… so this is a good example of how the optics, in this case passive optics, passive optical switching is doing some computation, some useful computation on the fly, you know, during transmission, because we have these data getting routed arbitrarily through the network, programmatically through the network.
gdeptuch: Yeah, so like I say, you can do this hierarchically, build it up layer by layer. People, they've demonstrated, in simulations and experiments, you know, several layers of hierarchy, so, and this lets you,
gdeptuch: in principle, go to arbitrary network sizes. And this is called CYPAC, the Silicon Photonic Accelerated Computer Architecture.
gdeptuch: So another example which is also related to this is doing more computation while the data is being transferred between, between computational unit and memory. So instead of the memory.
gdeptuch: bus being just a data transfer bus, it can do some processing while the data is being transferred, and this can reduce… this can do several things. For example, you can use this for compression, or you can use this for doing some reduction and some reduction in… and that can give you system-level advantages, such as reduction in latency or reduction in overall energy use.
gdeptuch: So you can imagine now we have these, as shown on the left, you have these photonic compute engines, which are leveraging 3D integration, as Gresh mentioned, which are now co-packaged with the optics, getting very, very high throughput, and processing data through these WDM buses as they're moving. So, one kind of concrete example of this, which they published recently, is doing
gdeptuch: There's a sort of innovative approach to doing multiply-accumulate operations, which are, as everybody knows, one of the core operations.
gdeptuch: For all these, AI engines. So the way this works is that you have, you have a multi-layered stack, again, 3D integration with, several layers of high-bandwidth memory DRAM stacked on top.
gdeptuch: Below that, you have an electrical IC, and below that is a photonic IC. The data, the coefficients that you want to multiply are stored in DRAM. They get transferred to the electrical IC, which calculates partial products, which are basically just multiplies. But then, when you want to do the addition, you transfer that to the photonic IC, which then does the addition. Moreover, the output light then goes back
gdeptuch: Naturally, and then is used.
gdeptuch: To look, to, to control the, the, the level of light, which can then, can tell you which then, through basically what, what is, what amounts to a flash type, to digital conversion, that says how many of the, outputs of the, of these, photodiodes are actually high or low, so that, that gives you the thermometer-coded output.
gdeptuch: Which means you have done this photonic analog-to-digital conversion.
gdeptuch: Quite efficiently in parallel, again, because we are doing this with, with a bunch of, resonators, which are… which are thermally tuned.
gdeptuch: So… so then we have this… okay, now in terms of performance achieved, this is still not at a high resolution. I mean, the… the,
gdeptuch: typical, like, number of bits is on the order of 3 to 4 as of now. You know, work is going on to increase that, but, but I'll say that already.
gdeptuch: for a lot of applications, even 4 bits of precision is good enough, and so this is a compelling way to actually implement these MAC operations and digitize in analog, and then digitize them efficiently in the organic domain.
gdeptuch: Yeah, so this is more of a blow-up of how that might actually be, implemented. Like I said, the high-bandwidth memory modules are on top. You have the IC, electrical IC in the middle, active photonic interposer, and then everything is sitting on a… and a package. Everything is then interposed on a substrate. And yeah, this is more of a blow-up of the algorithm. Like I said, partial products are actually… this is a good example, actually, of something
gdeptuch: that will be talked about in Thrust 3, which is how different operations might be partitioned between domains. You can see here the
gdeptuch: The multiplications are done, which is nonlinear operation are done electrically, but then the linear addition, which can be efficiently done optically, is done optically, so this might be a good example, or made a good point to actually hand over to the… to Thrust 3, which I think is coming up, and we'll talk more about partitioning.
gdeptuch: Yes. Okay, so the Trust Trust 3 will be shared between Prashant and Ocean, right? Yeah, yeah, okay, so I'll provide an overview, and then Ocean, who's from PNNL, will provide more details.
gdeptuch: So, in Trust 3, our objective really is to develop electrophotonic processing architectures and make use of all the different pieces, all the insights gained from Trust 1 and Thrust 2 in doing so.
gdeptuch: And for building these architectures, we'd like to leverage all the benefits of processing in photonic domain.
gdeptuch: And while we're trying to build these mixed electrophotonic processing architectures, our goal really is to also develop frameworks, tools, and methodologies which allow us to systematically develop these architectures following a top design… a top-down design approach as opposed to a bottoms-up approach.
gdeptuch: And we'd like to automate how we do our architecture search. We'd like to make the architecture search hardware aware, taking into account all the constraints coming from the processing platform that we use.
gdeptuch: And the target application for these processing architectures, one is developing optimized processing architectures for processing of data that's produced by detectors, but also something more generic, such as processing generic AI workloads.
gdeptuch: And one of the key principles that we follow in Trust 3 is that we'd like to optimally partition tasks between electrical and photonic domains, so as to best exploit the properties
gdeptuch: of each domain. And by doing that, we would like to model all our interfaces, both digital analog and electrical-optical interfaces, as well as the conversion costs associated with the interfaces.
gdeptuch: And also try and mitigate the conversion costs as we do the architecture search.
gdeptuch: the kind of architectures and applications that we are targeting.
gdeptuch: So, applications ranges… I mean, it's data processing, processing of the data that's produced by detectors at various points in the detector pipeline, ranging from, say, data compression and filtering, which is closer to the sensor, to developing low-latency feedback, as well as generating trigger signals at low latencies.
gdeptuch: And at the other end, it could be offloading tasks like particle track reconstruction, clustering calibrator hits, and doing this in a more energy-efficient manner than a typical electronic-only hardware accelerator would do.
gdeptuch: And then there are several choices for the processing architectures for doing one or multiple of these tasks. So, trust three, we'd like to explore both. First, the suitability of different architectures for doing these different compute operations, and also how photonics can be leveraged, and how photonics can be leveraged specific to each of these different architectures.
gdeptuch: The architectures range from, deep neural networks, where we implement dense MAC operations, striking neural networks.
gdeptuch: doing combinatorial optimization using icing machines, and also graph neural networks, which are of particular interest here. The reason being that the data that's produced by detectors is… it's often sparse, irregular, restructured, there are complex dependencies between the different pixels, different detector subsystems, and such data maps naturally to graphs.
gdeptuch: So we think graph neural networks can do a good job of processing the data that we typically see coming out of these detectors.
gdeptuch: And as I said, one of the key drivers of Thrust 3 is doing optimal electrophotonic design partitioning, and there's more coming on this from our colleagues in PNNL, but the approach here that we follow is that we try to implement optimization loops within our architecture search engine.
gdeptuch: Where we define performance, cost trade-offs, we define key metrics, which should be taken into account. These are some of the partitioning drivers that are shown here. Could be latency, power consumption, bandwidth throughput, how reliable is the architecture, as well as the noise resilience.
gdeptuch: And in the functional simulator that we developed, we do a hardware-avail behavioral modeling, so we try to model our components as close as possible to what we might realistically see.
gdeptuch: Given the, fabrication process, so we'd like to model non-idealities, crosstalk, losses, thermal drift, as well as conversion efficiencies, between different domains, and make use of all these different parameters as we do our design space exploration, as well as parameter optimization.
gdeptuch: As for task division, our initial starting point is allocating linear operations to photonic domain, because that's what photonic is really good at, doing nonlinear operations in electrical domain for the time being, until material advances allow us to do nonlinear operations efficiently in photonic domain. But this is just a starting point, and we hope to get more insights from our partitioning algorithm.
gdeptuch: And also make use of photonic interconnects wherever possible for doing energy-efficient data transmission, mitigating bottlenecks associated with moving data between the memory and the compute units.
gdeptuch: And as we evaluate these different architectures, wherever possible, we are trying to make use of publicly available physics-based datasets, which are all derived from experiment-like conditions. And our goal is to use these datasets for rigorous benchmarking of the processing architectures. One example is the TradCamel dataset that we're using
gdeptuch: For evaluating GNNs for doing particle trap reconstruction.
gdeptuch: And I think that's it for me. I'll pass over to Ocean. Yep, so we'll navigate, right? Is that okay if we navigate?
Oceane Bel (PNNL): Yeah, you guys can navigate. I'll just say next slide when, I need to get to the.
gdeptuch: Next slide.
Oceane Bel (PNNL): So, yeah, hi, I'm Ocean, I will be talking about what we've been working on at PNNL as part of Alpha. So, the partitioning problem that was mentioned earlier, we took it in two different directions. So, one.
Oceane Bel (PNNL): Is essentially what is the architecture we are looking at? Are we going to be looking at using certain components as electronic components and certain components as photonic components? Bear in mind that there are components that can be either photonic or electronic, so you also have to decide for us which
Oceane Bel (PNNL): portals as flexible components. So one of the ideas with that partitioning is that regular electronic computers are hitting a wall, they need a massive amount of power to run faster, whereas new photonic technology could help, but we don't know how to combine them with the electronic components quite yet in an effective manner.
Oceane Bel (PNNL): So, the tool that we are building, currently uses, Monte Carlo to essentially try different, combinations until it converges to us, a point based on the metrics that Prasancha mentioned earlier.
Oceane Bel (PNNL): And another approach that we're looking at is called hypergraph partitioning. This is a approach that our colleague Jason is bringing to the table and has been working on to integrate as part of our codebase.
Oceane Bel (PNNL): Basically, we're creating algorithms that will decide which computing task or which computing node would be what type. So, we would also work on allocating a certain task to a certain node, and whether that node is a photonic node or electronic node. So, it's a multi-dimensional problem that we're trying to break down and work step by step.
Oceane Bel (PNNL): Next slide.
Oceane Bel (PNNL): So, before I go too deep into the weeds of the technical approach, these are some of the libraries that we've been looking at. So, we've had, like, libraries like Meep that are used to simulate how light travels through photonic chips. We've looked at NG Spice, which simulates electronic circuits.
Oceane Bel (PNNL): Pymtl, which models digital logic, KLAL, PyTorch, GamePy. We've also explored some electronic photonic partitioning approach, such as MEDIS,
Oceane Bel (PNNL): I'm not going to try to pronounce the rest of them, it's on the side. But, yeah, we've also looked at those approaches, because we do want to get some knowledge of a baseline partitioning approach, specifically that has been developed for electronic photonic, so that once our tool has been fully developed, we can then compare it to existing state-of-the-art.
Oceane Bel (PNNL): Next slide, please.
Oceane Bel (PNNL): So, right now, we are working on a version of our partitioning tool that uses Monte Carlo Search to find a good combination of electronic component, photonic components, as you can see on the video here.
Oceane Bel (PNNL): We have both a baseline one, where we are just simply running a streamline of data and then seeing how we want to create the different partitioning approaches.
Oceane Bel (PNNL): We do generate graphs of the different partitioning that is generated.
Oceane Bel (PNNL): We also, give the user the ability to change the power or the latency as constraints, as part of our interface.
Oceane Bel (PNNL): We will be working on integrating more constraints later down the line. The code already has the capability, we just need to bring them up to the interface. We also do have a state of the code where we are essentially running not only Monte Carlo approach, where we're testing out the different performance factors of the different nodes.
Oceane Bel (PNNL): But we are also running and training the neural network model based on, essentially, the TensorFlow library, and then we're incorporating back the performance of the TensorFlow library into the resulting performance of the chip, and then that is taken into account for the loss of the model.
Oceane Bel (PNNL): And we're gathering the loss of the model by taking into account both the performance and the TensorFlow output. So that does take a while to finish.
Oceane Bel (PNNL): In the meantime, I'll also talk about another approach that will not be shown in the video quite yet, which is essentially
Oceane Bel (PNNL): The hypergraph approach, which I'll go more into, the next slide over.
Oceane Bel (PNNL): We will be integrating that one into,
Oceane Bel (PNNL): Oh, I think the video's over. Next slide, sorry.
gdeptuch: Excellent. Okay.
Oceane Bel (PNNL): So our second approach treats the problem like a puzzle. So, essentially, we represent the electronic and photonic components as dots, and their connections as lines linking multiple dots together, which create what we call a hypergraph.
Oceane Bel (PNNL): First, we simplify this complex puzzle by grouping related components together. We can then only group, components that belong to the same category or have a strong connection, kind of like, sorting puzzle pieces up by color or pattern.
Oceane Bel (PNNL): Next, we make a first guess as to how to divide everything between electronic and photonic parts, trying to keep things as balanced as possible. Again, we refer back to those metrics that we talked about at the beginning.
Oceane Bel (PNNL): And finally, we improved our solution by testing small changes. For each component. We asked, would moving this piece to the other side make things better? We focused on important improvements.
Oceane Bel (PNNL): First, using priority weight. This code is, currently in a working phase in the background of the code, of our codebase. We still have yet to connect it to our interface.
Oceane Bel (PNNL): But the goal would be that we would have this and the Monte Carlo approach connected to the interface, so that we can do, actually, a comparison between the partitioning approaches on both, and come up with a
Oceane Bel (PNNL): What we refer to as potentially an ensemble approach that, gives us a overwhelming positive partitioning solution that both includes task distribution and the selection of different components for different partitions.
Oceane Bel (PNNL): Thank you.
gdeptuch: Okay, so what about we now could close? I will share a couple, maybe, more slides, because we… to realize the goals of our Alpha project, of course, we…
gdeptuch: We need huge simulation engines, software, models, but also one cannot forget about investment in testing equipment, because having anything fabricated requires testing, and it's jumping from electrical domain to the photonic domain, electrophotonic domain.
gdeptuch: That has to be addressed, and we are working also on equipping our…
gdeptuch: laboratory spaces, so I think, certainly, this is… this slide is coming from the Columbia University, that, it's an electrophoton probe station for… allowing for dire wafer probing.
gdeptuch: investment of infrastructure for testing. It's also happening in our… at our colleague… colleague's facility at CUNY CCNY, and our colleagues at Florida University are investing in finite element methods simulations… simulation tools, and at Brookhaven, we are also investing in a new laboratory space that is,
gdeptuch: We call it a new test lab facility, located at Instrumentation department, where we already have 12-inch semi-automatic probe stations. Now we're adding
gdeptuch: photonic package on top of it, where we'll be able to test electrical and photonic chips on the wafers, on single dice, with edge coupling of single fibers, ribbon, ribbon fibers.
gdeptuch: So all that is needed later on to explore, especially, particularly at, aimed at 3D integrated stacks, that will be needed to access such stacks with very high precision. So this investment is ongoing. We're also looking into development of in-house capabilities such as photonic wire bonding.
gdeptuch: To have, pop-up, through,
gdeptuch: using polymeric wires to be able to interconnect photonic photonic ICs. So these investments are happening in parallel. They are part of the alpha project, but also generally enhancement of capabilities and instrumentation that Department and the Brookhead National Laboratory.
gdeptuch: But this brings me also to… this brings me into summary, and in summary, I would like to say that ELFO is a very intense project, covers… spans a full chain from concept to… through design, fabrication, finally, to testing. We cover manufacturing of electrophotonic circuit prototypes, we pursue heterogeneous integration, we require advanced test and characterization infrastructure.
gdeptuch: structure.
gdeptuch: We drive development of new CAD EDA tools and methodologies for the designing, and when I was talking about special investment in the testing architecture, or testing infrastructure, certainly it's great to be part of a Meerkat Center, because certainly this is why we are having all these presentations.
gdeptuch: happening when, in front of Meerkat Center members. We seek for… we seek for synergies.
gdeptuch: within Meerkat and also outside, so we enlarge on external foundry facilities. We mix commercial and open source design flows, and we have huge exposure, you know, overlap already we can notice with Deco D project, that is another project within Meerkat. I would like to also stress that was a great experience for me, particularly
gdeptuch: attending, you know, the SINT annual meeting that was… that took place in Santa Fe a bit more than one month ago, when there was a lot of exposure on what is going on within the centers, and also we've invited guests, and thanks to Jeff. So I think the synergies
gdeptuch: and reusing maybe some investments among Meerkat Center members could be a great addition to our L4 project, and as I mentioned, we have very intense plan in front of us, and hope we'll be able to realize it.
gdeptuch: And, hopefully we'll be… we'll be able to grow, next steps out of this, what I'm… what I'm seeing as a planted seed, kind of incubate… incubate it to broader, broader efforts. So I think we managed to be on time, so this is the last slide, and thank you for your attention.
gdeptuch: Or if there are questions, we try to answer.
gdeptuch: Or we take notes.
gdeptuch: Yeah, we lost people, I guess.
Paul McIntyre: Hey there. Yeah, thanks so much, Gruz Gors, that was terrific. Thank you to your team as well. Really a lot of cool stuff in this, project.
Paul McIntyre: Any, any questions?
Paul McIntyre: I guess maybe I'll kick it off with a question. Can you give us an idea of how this effort kind of compares with
Paul McIntyre: others that might be going on in industry that you're aware of, or in other consortia. Clearly, you're addressing some really
Paul McIntyre: Critical, topics that are relevant to many different kinds of
Paul McIntyre: of applications in computing more broadly. So is there a… is there a fairly well-developed set of projects out there that are doing other things, or is this pretty unique?
gdeptuch: Oh, I would maybe partially answer the question, and I later ask my colleagues to add, because
gdeptuch: that everyone may have different… slightly different opinions. I would say that when we started… this is why I brought the motivation slides at the beginning, because a few years ago, when we started talking about reaching for photonics, we were told, or we actually also were thinking that, oh, photonics is… photonics is great for beta transmission, because you cannot stop photons, you cannot do computation. Slowly.
gdeptuch: we start seeing, you know, many efforts emerging or, you know, advancing, especially maybe more at universities, at the first place, to use photonics for computation. You may, of course, as Prashansa mentioned, consider, you know, operating purely on amplitude. You can also work with phase, with interferometric, you know, approaches.
gdeptuch: Of course, you don't stop photons, but you can get these linear operations, multiplication and addition very efficiently and very fast.
gdeptuch: And what is actually exceeding speed of these computations?
gdeptuch: even up to 2 orders of magnitude compared to electrical domain, is wavelength division multiplexing. You cannot… you cannot imagine on a single wire to have, you know, 100 frequencies multiplexed, and then demultiplex them, because, you know, resonance circuit will be gigantic, and will be bad. But in photonics, yeah, it starts to be possible. So you can get a factor of acceleration 10X, 100x.
gdeptuch: in computation. Whether it is something unique or not.
gdeptuch: probably it is not… it cannot be said it is totally unique, because, for example, efforts that Columbia colleagues are doing is oriented on the data center, and they are operating on funding from companies that are either, you know, investing towards the future.
gdeptuch: Or, you know, aiming at new technology development. And we started seeing that in photonic computing.
gdeptuch: is really emerging, and the platforms, particularly silicon photonics, started to be available, where you had all elements needed for linear operations. You start seeing, also, the platforms that are
gdeptuch: non-silicon-based, where you can mix nonlinear operations. I will not be going into these details, because probably it will take a bit more time than needed. You have also heterogeneous embedment of, for example, lasers in the silicon photonics, so these things are trends, I think, in the industry, but we try to have our L4 project
gdeptuch: aimed at what DOE could be interested in.
gdeptuch: And of course, if something happens that we are able to sell this, our findings.
gdeptuch: to industry, it would be… it would be a great, greatest achievement. I don't know, you want to add anything? The only other thing is about… so part of TELFO is also focused on developing tools and methodologies, and for photonics specifically, commercially available EDA tools are not as mature as electronics, so we are hoping that some of the R&D that we do can eventually become part of commercial tools.
gdeptuch: Is it good? Yep.
Paul McIntyre: That makes a lot of sense.
gdeptuch: or what Prashant was covering in her part, you know, partitioning.
gdeptuch: you know, what to cover in photonic domain and what to cover in electrical domain. We are at the very early stage of that, because we are still discussing how to define weight function, you know, to… for the automated optimization. But when we get it somehow, I mean, this certainly can trigger, I hope, huge interest from industry to get… to embed it in regular tools. And of course, now we are focusing on
gdeptuch: partitioning between photonic and electrical domain using known components that exist in the silicon photonic platform, but we can expand it. And, you know, the most important thing is that we skip handmade
gdeptuch: photonic layouts, photonic implementations, because now when you go into Cadence and you start using, you know, even the very sophisticated tool, like Kirby Core to avoid drawing waveguides, you have to do it by hand. But we were able to automate the process from top to bottom, from the concept of GNN, like Prashansa was describing.
gdeptuch: through partition in between domains, and then, like, synthesis in the electrophotonic domain. I think this can be breakthrough. Whether we'll achieve it, we'll see. Maybe in collaborations with others, this can be a huge addition to automate, effectively, this type of efforts. I don't know…
gdeptuch: Anyone wants to add?
gdeptuch: We can ask our advisor, industrial advisor, if you have any… Bob, do you have anything to add?
'Pops' / NY CREATES: No, no, no, I don't think I have much to add. I know you're working closely with Infotonics and
'Pops' / NY CREATES: Nope, nothing much to add.
Paul McIntyre: Okay, any other questions?
Paul McIntyre: Well, if not, we're pretty much at time, so thanks so much to the Alpho team for a great presentation. There was a lot there, and we're going to closely, review the recording of this. I want to share this with some of our colleagues in my team, because I think they'll really,
Paul McIntyre: benefit from seeing it, and there's some… I see some opportunities for collaborations that could emerge
Paul McIntyre: And so it's been great to see this, and really exciting to see what your team does during this, this Meerkat Center project.
Valerie Taylor: Yes, indeed. Yes, we have some, someone that's doing work in,
Valerie Taylor: optical interconnects, so I definitely want to share, the recording.
Valerie Taylor: There, as well.
gdeptuch: So, I think we're happy to, you know, go maybe in more details, if someone is interested in kind of going in the details, because we actually do high-speed jet… jet…
gdeptuch: fly over what we plan, but we wanted to cover the whole…
gdeptuch: I, you know, set part of ideas that we are having, going, as I said earlier, from materials, modification, but needed, to all the way to high-level abstraction randomization of models for computing.
jsnelso: Horace, I'm sure you're still talking with Rakdam here, just making.
gdeptuch: Yes, yes, we are planning to have what I call a workshop.
gdeptuch: invite him. He shared with us school staff what he's doing, and effectively, we already found… I mean, at least I found synergies, because tackle… tackling all the problems of on-the-flight processing in photonic domain, he was showing certain things for images that he's interested, you know, some objects, identification, and this is exactly what,
gdeptuch: It's, common between, you know, his work and, for example, what we have in particle physics, even, that we have
gdeptuch: something is being… something's changing quickly, and you would like to catch that. I think what he was… particularly what he was showing to us was great achievements in compression of sparse data sets. This is… this is something that we would like to look closer, working with, you know, with him.
jsnelso: Okay, great. Thanks.
Paul McIntyre: Cool.
Paul McIntyre: Thanks so much. I've got to jump onto another call, but this was terrific, and we'll have a PI-only discussion next week. We can talk about those two agenda items, the, you know, initial planning for an annual meeting.
Paul McIntyre: And also, let me see… oh, the, the, the, our,
Paul McIntyre: our subcommittees, or our sub-teams, and how we want to populate those. So we'll, we'll,
Paul McIntyre: We'll pick that up, next time, and be on the lookout for a new… I think we'll… you'll see… you should see these, the current calendar invitations, deleted and replaced by, new ones, starting, for next Wednesday.
gdeptuch: Very good. I'm glad that we didn't go over time too much.
Paul McIntyre: Okay, thank you.
'Pops' / NY CREATES: Thank you again.
Valerie Taylor: Thank you!