Faraj Aalaei of Cognichip is founder and CEO and presents the company’s launch of artificial chip intelligence, abbreviated ACI, a physics-informed full-stack system for chip design that optimizes artificial intelligence workloads. Aalaei explains that the platform integrates end-to-end model training, workflow integration and a software-driven design flow that compresses years of semiconductor expertise into an automated development process. They describe how ACI collapses serial siloed workflows into a unified spherical system to accelerate development and enable bespoke low-power devices at software speed.
Aalaei asserts that ACI reduces chip development time roughly tenfold by collapsing serial siloed workflows into a unified spherical system. The platform aims to accelerate edge AI adoption, support enterprise deployment of AI hardware, protect intellectual property and enable rapid FPGA programmability. Open weights and secure on-prem deployments are strategic priorities for Cognichip as customers seek faster time to market and stronger IP protection.
The conversation is presented by theCUBE Research and is hosted by John Furrier and David Vellante. Relevant topics covered include chip design, semiconductor engineering, physics-informed modeling, full-stack infrastructure, model training and workflow integration for hardware AI development.
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
theCUBE + NYSE Wired: AI Factories - Data Centers of the Future. If you don’t think you received an email check your
spam folder.
Sign in to AI Factories - Data Centers of the Future.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open the link to automatically sign into the site.
Register for AI Factories - Data Centers of the Future
Please fill out the information below. You will receive an email with a verification link confirming your registration. Click the link to automatically sign into the site.
You’re almost there!
We just sent you a verification email. Please click the verification button in the email. Once your email address is verified, you will have full access to all event content for AI Factories - Data Centers of the Future.
I want my badge and interests to be visible to all attendees.
Checking this box will display your presense on the attendees list, view your profile and allow other attendees to contact you via 1-1 chat. Read the Privacy Policy. At any time, you can choose to disable this preference.
Select your Interests!
add
Upload your photo
Uploading..
OR
Connect via Twitter
Connect via Linkedin
EDIT PASSWORD
Share
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
theCUBE + NYSE Wired: AI Factories - Data Centers of the Future. If you don’t think you received an email check your
spam folder.
Sign in to AI Factories - Data Centers of the Future.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open the link to automatically sign into the site.
Sign in to gain access to theCUBE + NYSE Wired: AI Factories - Data Centers of the Future
Please sign in with LinkedIn to continue to theCUBE + NYSE Wired: AI Factories - Data Centers of the Future. Signing in with LinkedIn ensures a professional environment.
Are you sure you want to remove access rights for this user?
Details
Manage Access
email address
Community Invitation
Faraj Aalaei, Cognichip
Faraj Aalaei of Cognichip is founder and CEO and presents the company’s launch of artificial chip intelligence, abbreviated ACI, a physics-informed full-stack system for chip design that optimizes artificial intelligence workloads. Aalaei explains that the platform integrates end-to-end model training, workflow integration and a software-driven design flow that compresses years of semiconductor expertise into an automated development process. They describe how ACI collapses serial siloed workflows into a unified spherical system to accelerate development and enable bespoke low-power devices at software speed.
Aalaei asserts that ACI reduces chip development time roughly tenfold by collapsing serial siloed workflows into a unified spherical system. The platform aims to accelerate edge AI adoption, support enterprise deployment of AI hardware, protect intellectual property and enable rapid FPGA programmability. Open weights and secure on-prem deployments are strategic priorities for Cognichip as customers seek faster time to market and stronger IP protection.
The conversation is presented by theCUBE Research and is hosted by John Furrier and David Vellante. Relevant topics covered include chip design, semiconductor engineering, physics-informed modeling, full-stack infrastructure, model training and workflow integration for hardware AI development.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street. I'm John Furrier, host of theCUBE here with David Vellante, my co-host. Hello, I'm John Furrier, host of theCUBE here at theCUBE's New York Stock Exchange studio. Of course, we are Palo Alto Studio connecting Silicon Valley to Wall Street. This is our AI Factory series. We talk to the leaders who are making it happen, who are helping expand the AI era. The AI infrastructure and all that is enabled and accelerated on top of it agents, physical AI, and the need for semiconductors, the need for systems is in high demand. And this next segment will address that. Cognichip CEO Faraj Aalaei, welcome to theCUBE. Thanks for coming on. Appreciate it.
Faraj Aalaei
>> Thanks for having me, John.
John Furrier
>> Cognichip, you guys are now announcing your products. It's been stealth for a while. You might have solved the biggest problem that everyone wants, smaller, faster, cheaper, kind of in the Intel Moore's Law way is what people want. They want smaller form factor for AI. You guys have a chip. Talk about what you guys are doing. What is the product? What is the vision?
Faraj Aalaei
>> Well, so what we're building is really the industry's first in 4 different areas, John. First of all, we are training our own models, which are physics-informed. Meaning that they understand chip design, transistor design, and how it all comes together to make a product. And that's the fundamental difference than essentially general-purpose LLMs that get bolted onto some kind of a chip flow. This is built from the bottom up for the chip industry. The second part of this is that we put all of this in an end-to-end system, an end-to-end infrastructure that allows a designer to get in and start from idea generation all the way up to a complete chip design in one system. And that helps you build, products in a much shorter time, right? We think that we can, for a given task, probably break it down by a factor of 10 in terms of time collapse. And then of course the cost of developing that product gets significantly reduced, time to market improves, and all of that. The third part of this whole thing is the compute infrastructure is how do you take a technology for the last 30 or 40 years has been put together with serial format, essentially in silos, and now collapse it into what we refer to as a spherical approach where you're optimizing around all the elements of a chip design into one system at a reduced time. And then last but not least, It's a system, it's an infrastructure design infrastructure system that understands workflows, chip workflows, the intuition that's built by an engineer that has worked for 30 years into this system. So not only can it get you there faster and can get you there cheaper, it can get you there with confidence that you're designing the right kind of product and there is no hallucination in this kind of a—
John Furrier
>> So you've got AI everywhere on the chip side to help you guys do that faster. But also it's a full-stack intelligent chip. Now I want to ask one question if you don't mind, because I hear this all the time, certainly here in New York everyone talks about, AI, tape-out. In Silicon Valley and on SiliconANGLE.com, my site, as we cover the Valley. We've been seeing the chips evolution. It's a long process. Tape-out, TSMC, I hear people, well, we got to get more chips. And it's literally years. Is the innovation, the speed of chip building, What specifically is it that you guys have? Is it the speed to get a chip? So if I'm building something, a robot, I just get my own chip. Is that kind of what it is?
Faraj Aalaei
>> So what we're building is actually a software product that helps designers get the chip done with a lot less resources in a shorter period of time. And yeah, you can actually build any chip you want. Our customers are semiconductor companies or frankly anybody that builds chips these days, which includes all the system guys, the cloud guys and so on. And so with this software, you can actually just interface with it like you do with any kind of model today. It's really a foundation model for chip design. That's the way you should think about it. And yeah, as such, you don't have to be a chip expert anymore to design chips. You can have an idea and a concept about how to approach a market and what a market needs, whether it be in the physical AI or whether it be in some kind of enterprise infrastructure. You can use this system to build your chips.
John Furrier
>> Faraj Aalaei, it's interesting because if you're an engineer and you're in the semi, you know how hard it is to build a chip. It takes years before you even see a chip. Then you got to put it into trials, take it to customers. And then you start making money in volume. We're in a highly accelerated market right now. Even things like quantum computing, which was a fantasy just a few years ago, it's accelerating fast. AI has come on the scene. You've seen the models come in fast. Share your thoughts on the speed because if you asked someone 5 years ago, I can build the chip in a month, what are you, crazy? Talk about the velocity of the innovation cycle now and why is it possible to do this now?
Faraj Aalaei
>> Well, let me start, John, by saying that I've built many chips. When you start to design a chip, this chip isn't going to be in production for another 5 years because it takes you a couple of years to build it. That's been the story so far. And through it, the semiconductor industry has done actually quite well. But in order to keep this growth and given how fast software actually steps up, hardware needs to catch up and we can no longer go on taking 2, 3 years to design chips. It needs to be shrunk. And this system allows you to do all of that and create bespoke devices. Now bespoke devices have other capabilities, meaning that they're lower power, they're better performing devices and lower cost for the marketplace. So that kind of keeps that whole cycle going of innovation on the chip side, feeding innovation on the software, and you have a virtuous cycle. That's happening
John Furrier
>> with—we talk a lot on theCUBE and on SiliconANGLE too, we write about it a lot, which is, when you see these big data centers, the GPUs, there's a lot of horsepower involved, a lot of density, pick your approach, power density. When you look at the innovation around the bottlenecks, because there's a lot of scarcity of resource, chips, supply's constrained, engineers will go to the constraints and they'll engineer around it. And one of them is, how do I get the same performance out of it with a different set of hardware and software. And I think you're starting to see that. You guys are doing that. You're essentially coming in and filling in a demand cycle that's saying, hey, if you want performance— and by the way, it's distributed computing, so you can actually have it all connected. Talk about this dynamic of you don't have to have the big iron, the big clusters to do some of the things that are required in AI.
Faraj Aalaei
>> Yeah, with ACI, in principle, you can go two ways. You can build a bespoke device that gets better performance, lower power in a much shorter period of time. Or in case of Physical AI, you can actually take a model weight and pull it into hardware within a matter of days and not even weeks or months. And that solves a huge problem on the edge because on the edge, If the information keeps changing, you can't keep just yanking the hardware out and replace it with a different hardware. So you need some amount of programmability in there. But that programmability has to be enabled in a way that it connects the model weights to circuits running in hardware with low latency and high performance. And that's the magic that this technology provides.
John Furrier
>> So what about the open weights? There's been a lot of discussion on open weights. Obviously, open source has been a big driver of the innovation. What's the importance for open weights in this paradigm? Because You now have open source, free. Now you have solutions. You mentioned some of those weights. Do open weights play into that and what's the impact of that?
Faraj Aalaei
>> I think open weights are crucially important to the industry as a whole. in a lot of cases, a lot of manufacturers want to be in control of that piece of software or the weights of the model. And open weights allow them to stay independent and move at their own pace for their product evolution. Of course, closed source models also provide certain capabilities. Every industry is different, right? And so we should have a mix of both. And that's a good thing. Yeah, it's a good thing. It's fantastic. It creates applications a lot faster and makes it easier to build valuable products out in the marketplace that serve a purpose.
John Furrier
>> You know, one of the things we cover a lot is the enterprise. And obviously edge is exploding now, we'll see a lot more this year. You know, a classic enterprise doesn't have billions of dollars to spend on CapEx. So they're going to look for open weights, they're going to look for the kind of solutions that would fit into a classic data center. They'll probably connect to a hyperscaler, they'll probably connect to a NeoCloud service, but that's not the end game. There's going to be a lot more action going on on the premises.
Faraj Aalaei
>> Absolutely.
John Furrier
>> Faraj, share your thoughts on how you see that developing and how you guys fit into that?
Faraj Aalaei
>> Well, look, several industries, defense industry as an example, semiconductor industry where we play a big role there, they have very high expectations for information security and for being able to essentially have air gap systems, right? And so open-weight solutions, provide you the ability to take those and make them your own, build your system architecture around them and keep them proprietary, the total solution proprietary. And certainly, we're an enabler of that. And even in our own case, for example, we are developing our own physics-informed models. Those models are really important to our customers in the sense that they know they can use them and they know all of that information stays private and where it is in their environment. And that's a critical point for the semiconductor industry because in the semiconductor industry, your IP is everything you have, right? And you do not want to be sending that here and there and not knowing where it goes, right? So we give these customers the comfort that they can get maximum intelligence in chip design while protecting their IP at all times.
John Furrier
>> Talk about what it means to be physics-informed, because I think this is a unique feature. Also workload-aware. You guys have that kind of an intelligent chip What does that mean, physics-informed? It just knows everything about physics or specifically about the materials? what does that mean?
Faraj Aalaei
>> Yeah, it's a very good question. So, general-purpose LLMs are built essentially to answer any question you have, right? When you come into industries like— and the way they have that knowledge and intelligence is because they've trained on gobs and gobs of data that has been collected everywhere. Well, there isn't gobs and gobs of data for semiconductor industry. So a model, a general purpose built model, does not know how to deal with the physical reality of a transistor. Right. So what we do is the way we train our models is make sure that the models that we're building understand transistors, understand it has a physical formation, it has power capabilities, it has frequency capabilities. And so in order to then be able to start from architecture level and end up at the chip, if you're coding for a chip, you need to know what this thing is going to look like once it gets synthesized into transistors. If you don't have that, you can't build models that are good in semis. So what we've done is we've invested since day one in building the largest data moat in the semiconductor industry and use those data stacks that we've created in training bespoke models for chip design. So it is physics-informed because the moment you start to say, I want to do X, Y, and Z, it understands how that's going to translate into transistors on a chip. And that is the magic.
John Furrier
>> It's like having a team of engineers on staff.
Faraj Aalaei
>> Absolutely.
John Furrier
>> That's what basically it turns into.
Faraj Aalaei
>> Team of engineers who are expert across the semiconductor field, across all various applications at your fingertips. So it's really a digital co-designer.
John Furrier
>> All right, let's get into the news. You guys are launching the Artificial Chip Intelligence, ACI Enterprise, world's first full-stack intelligence infrastructure for chip design. That's the tagline, that's the sentence. Take us through the product, what your plans are, target customer, is it a designer? we're hearing OpenAI's designing their own chip, Anthropic's building their own chip. Everyone wants to build their own chips. I'm like, well, good luck with that. But now—
Faraj Aalaei
>> And we're here to serve all of them.
John Furrier
>> Take us through the value proposition, the product opportunity for you guys.
Faraj Aalaei
>> Sure. So artificial chip intelligence, ACI, is really— if you think about AGI, we are ACI, which means we're focused on the chip and chip intelligence. And the way we bring that to bear is that we combine that with very detailed workflows on top of these models. And the customer can start by saying, I have this idea, or I have this specific IP, I want to build a chip. And we take them through all the steps that the industry has been going through for 30 years, but we do it at blazing speed. We do it at 10x the speed at minimum. And so what does it do? It allows you to code in chip language. It allows you to synthesize this, simulate it, synthesize it, and create something that meets your requirements for power, performance, and area. And that's never been done before. And you can do this at software speed, really.
John Furrier
>> You know, there's an old expression in the industry, we've said it many times on theCUBE, it's quoted most by Andy Jassy, who's the CEO of Amazon. He says, "There's no compression algorithm for experience." Actually, there is. You guys are doing it. You guys have this specialism with the semis. Specialized intelligence is the hottest area around. General intelligence is great, I want to know what's going on in the world. The frontier models crawl the internet. They know all the general answers. So AGI, whatever.
Faraj Aalaei
>> That's right.
John Furrier
>> But now specialized intelligence. Talk about the impact because you guys are essentially doing that and you have specialized intelligence on the chip, chip design, all the process management, process experience. You guys have compressed that.
Faraj Aalaei
>> Yeah, we have compressed that. So think about it this way, right? Today, if you want to design a chip, it's going to take you hundreds, if not thousands of engineers, depending on how complicated the chip is, and it's going to cost you several hundred million dollars to build a chip. What we've set out to do is to change that entire game, change the industry in that way, in that you don't need thousands of engineers to design a chip. You need very few, but you need an intelligent AI enterprise system and you will arrive at the same chip, perhaps more bespoke and perhaps even better performance. At a fraction of the time. This changes our industry because now you can go from idea to shipping product at close to the speed that people develop software and take it to production. Right. And close that gap. And the more you close that gap, the more profitable semiconductor companies are going to be.
John Furrier
>> Talk about your background. It's not your first rodeo in this area. And bespoke was always kind of a— I won't say bad word, but people would roll their eyes. I'm building a bespoke system. Whoa, no, we want to have a broad market opportunity. This is a unique paradigm shift.
Faraj Aalaei
>> Yeah.
John Furrier
>> What's it like? compare it to what you've done in the past. You have multiple chip companies. Yeah.
Faraj Aalaei
>> So I've built and taken two semiconductor companies public. And what I tell you is because of the long time that it took to design chips, we pack a lot of stuff in these chips. We make them as general purpose as we can. That comes with cost, that comes with the cost in terms of time to market. It comes in terms of actually the cost of the device. And so in the world today where we have different models and different workloads, actually being able to build a general purpose or build a bespoke chip. But if you want to build a bespoke chip, you're going to have advantages in the marketplace because it is going to operate at the performance you want and doing the kinds of things you want. And not much more than that, because now if the world changes, you can react to it very quickly. It's not like the old times where I have to wait those 5 years to get a chip into production.
John Furrier
>> Yeah. And the programmability you mentioned earlier, talk about that aspect of that requirement in today's world. Yeah.
Faraj Aalaei
>> So, you can build programmable devices obviously today. FPGAs, I think, are going to be well positioned, by the way, in this marketplace. For stuff that happens at the edge in the physical AI field. And being able for us to take from idea to an FPGA program that runs hardware at speed, it can happen in a matter of hours to days. And that closes the loop and really being able to bring these products to marketplace and changing their behavior on the fly almost.
John Furrier
>> Got it. Final question for you. Now that you guys are launched, what's the focus? What's your plan?
Faraj Aalaei
>> So our plan is to expand our market reach with our customers. We're engaged with 45 customers today, and this is just the beginning of our product introduction. Our plan is to continue to make our model more and more intelligent, continue to add features and capabilities, and really make this system easy to use where you don't need to be an electrical engineer to use this system to arrive at the solution for chip design.
John Furrier
>> So congratulations and looking forward to catching up after the launch.
Faraj Aalaei
>> Thank you very much.
John Furrier
>> I'm John Furrier with theCUBE. AI factories, again, the innovation, the engineers will go to the constraints and one of them is small form factors, high performance edge robotics. Starting to see a lot more activity. More power is packed into the system when you have the smaller, faster programmable devices. Again, another example of the innovation here on theCUBE. Thanks for watching.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street. I'm John Furrier, host of theCUBE here with David Vellante, my co-host. Hello, I'm John Furrier, host of theCUBE here at theCUBE's New York Stock Exchange studio. Of course, we are Palo Alto Studio connecting Silicon Valley to Wall Street. This is our AI Factory series. We talk to the leaders who are making it happen, who are helping expand the AI era. The AI infrastructure and all that is enabled and accelerated on top of it agents, physical AI, and the need for semiconductors, the need for systems is in high demand. And this next segment will address that. Cognichip CEO Faraj Aalaei, welcome to theCUBE. Thanks for coming on. Appreciate it.
Faraj Aalaei
>> Thanks for having me, John.
John Furrier
>> Cognichip, you guys are now announcing your products. It's been stealth for a while. You might have solved the biggest problem that everyone wants, smaller, faster, cheaper, kind of in the Intel Moore's Law way is what people want. They want smaller form factor for AI. You guys have a chip. Talk about what you guys are doing. What is the product? What is the vision?
Faraj Aalaei
>> Well, so what we're building is really the industry's first in 4 different areas, John. First of all, we are training our own models, which are physics-informed. Meaning that they understand chip design, transistor design, and how it all comes together to make a product. And that's the fundamental difference than essentially general-purpose LLMs that get bolted onto some kind of a chip flow. This is built from the bottom up for the chip industry. The second part of this is that we put all of this in an end-to-end system, an end-to-end infrastructure that allows a designer to get in and start from idea generation all the way up to a complete chip design in one system. And that helps you build, products in a much shorter time, right? We think that we can, for a given task, probably break it down by a factor of 10 in terms of time collapse. And then of course the cost of developing that product gets significantly reduced, time to market improves, and all of that. The third part of this whole thing is the compute infrastructure is how do you take a technology for the last 30 or 40 years has been put together with serial format, essentially in silos, and now collapse it into what we refer to as a spherical approach where you're optimizing around all the elements of a chip design into one system at a reduced time. And then last but not least, It's a system, it's an infrastructure design infrastructure system that understands workflows, chip workflows, the intuition that's built by an engineer that has worked for 30 years into this system. So not only can it get you there faster and can get you there cheaper, it can get you there with confidence that you're designing the right kind of product and there is no hallucination in this kind of a—
John Furrier
>> So you've got AI everywhere on the chip side to help you guys do that faster. But also it's a full-stack intelligent chip. Now I want to ask one question if you don't mind, because I hear this all the time, certainly here in New York everyone talks about, AI, tape-out. In Silicon Valley and on SiliconANGLE.com, my site, as we cover the Valley. We've been seeing the chips evolution. It's a long process. Tape-out, TSMC, I hear people, well, we got to get more chips. And it's literally years. Is the innovation, the speed of chip building, What specifically is it that you guys have? Is it the speed to get a chip? So if I'm building something, a robot, I just get my own chip. Is that kind of what it is?
Faraj Aalaei
>> So what we're building is actually a software product that helps designers get the chip done with a lot less resources in a shorter period of time. And yeah, you can actually build any chip you want. Our customers are semiconductor companies or frankly anybody that builds chips these days, which includes all the system guys, the cloud guys and so on. And so with this software, you can actually just interface with it like you do with any kind of model today. It's really a foundation model for chip design. That's the way you should think about it. And yeah, as such, you don't have to be a chip expert anymore to design chips. You can have an idea and a concept about how to approach a market and what a market needs, whether it be in the physical AI or whether it be in some kind of enterprise infrastructure. You can use this system to build your chips.
John Furrier
>> Faraj Aalaei, it's interesting because if you're an engineer and you're in the semi, you know how hard it is to build a chip. It takes years before you even see a chip. Then you got to put it into trials, take it to customers. And then you start making money in volume. We're in a highly accelerated market right now. Even things like quantum computing, which was a fantasy just a few years ago, it's accelerating fast. AI has come on the scene. You've seen the models come in fast. Share your thoughts on the speed because if you asked someone 5 years ago, I can build the chip in a month, what are you, crazy? Talk about the velocity of the innovation cycle now and why is it possible to do this now?
Faraj Aalaei
>> Well, let me start, John, by saying that I've built many chips. When you start to design a chip, this chip isn't going to be in production for another 5 years because it takes you a couple of years to build it. That's been the story so far. And through it, the semiconductor industry has done actually quite well. But in order to keep this growth and given how fast software actually steps up, hardware needs to catch up and we can no longer go on taking 2, 3 years to design chips. It needs to be shrunk. And this system allows you to do all of that and create bespoke devices. Now bespoke devices have other capabilities, meaning that they're lower power, they're better performing devices and lower cost for the marketplace. So that kind of keeps that whole cycle going of innovation on the chip side, feeding innovation on the software, and you have a virtuous cycle. That's happening
John Furrier
>> with—we talk a lot on theCUBE and on SiliconANGLE too, we write about it a lot, which is, when you see these big data centers, the GPUs, there's a lot of horsepower involved, a lot of density, pick your approach, power density. When you look at the innovation around the bottlenecks, because there's a lot of scarcity of resource, chips, supply's constrained, engineers will go to the constraints and they'll engineer around it. And one of them is, how do I get the same performance out of it with a different set of hardware and software. And I think you're starting to see that. You guys are doing that. You're essentially coming in and filling in a demand cycle that's saying, hey, if you want performance— and by the way, it's distributed computing, so you can actually have it all connected. Talk about this dynamic of you don't have to have the big iron, the big clusters to do some of the things that are required in AI.
Faraj Aalaei
>> Yeah, with ACI, in principle, you can go two ways. You can build a bespoke device that gets better performance, lower power in a much shorter period of time. Or in case of Physical AI, you can actually take a model weight and pull it into hardware within a matter of days and not even weeks or months. And that solves a huge problem on the edge because on the edge, If the information keeps changing, you can't keep just yanking the hardware out and replace it with a different hardware. So you need some amount of programmability in there. But that programmability has to be enabled in a way that it connects the model weights to circuits running in hardware with low latency and high performance. And that's the magic that this technology provides.
John Furrier
>> So what about the open weights? There's been a lot of discussion on open weights. Obviously, open source has been a big driver of the innovation. What's the importance for open weights in this paradigm? Because You now have open source, free. Now you have solutions. You mentioned some of those weights. Do open weights play into that and what's the impact of that?
Faraj Aalaei
>> I think open weights are crucially important to the industry as a whole. in a lot of cases, a lot of manufacturers want to be in control of that piece of software or the weights of the model. And open weights allow them to stay independent and move at their own pace for their product evolution. Of course, closed source models also provide certain capabilities. Every industry is different, right? And so we should have a mix of both. And that's a good thing. Yeah, it's a good thing. It's fantastic. It creates applications a lot faster and makes it easier to build valuable products out in the marketplace that serve a purpose.
John Furrier
>> You know, one of the things we cover a lot is the enterprise. And obviously edge is exploding now, we'll see a lot more this year. You know, a classic enterprise doesn't have billions of dollars to spend on CapEx. So they're going to look for open weights, they're going to look for the kind of solutions that would fit into a classic data center. They'll probably connect to a hyperscaler, they'll probably connect to a NeoCloud service, but that's not the end game. There's going to be a lot more action going on on the premises.
Faraj Aalaei
>> Absolutely.
John Furrier
>> Faraj, share your thoughts on how you see that developing and how you guys fit into that?
Faraj Aalaei
>> Well, look, several industries, defense industry as an example, semiconductor industry where we play a big role there, they have very high expectations for information security and for being able to essentially have air gap systems, right? And so open-weight solutions, provide you the ability to take those and make them your own, build your system architecture around them and keep them proprietary, the total solution proprietary. And certainly, we're an enabler of that. And even in our own case, for example, we are developing our own physics-informed models. Those models are really important to our customers in the sense that they know they can use them and they know all of that information stays private and where it is in their environment. And that's a critical point for the semiconductor industry because in the semiconductor industry, your IP is everything you have, right? And you do not want to be sending that here and there and not knowing where it goes, right? So we give these customers the comfort that they can get maximum intelligence in chip design while protecting their IP at all times.
John Furrier
>> Talk about what it means to be physics-informed, because I think this is a unique feature. Also workload-aware. You guys have that kind of an intelligent chip What does that mean, physics-informed? It just knows everything about physics or specifically about the materials? what does that mean?
Faraj Aalaei
>> Yeah, it's a very good question. So, general-purpose LLMs are built essentially to answer any question you have, right? When you come into industries like— and the way they have that knowledge and intelligence is because they've trained on gobs and gobs of data that has been collected everywhere. Well, there isn't gobs and gobs of data for semiconductor industry. So a model, a general purpose built model, does not know how to deal with the physical reality of a transistor. Right. So what we do is the way we train our models is make sure that the models that we're building understand transistors, understand it has a physical formation, it has power capabilities, it has frequency capabilities. And so in order to then be able to start from architecture level and end up at the chip, if you're coding for a chip, you need to know what this thing is going to look like once it gets synthesized into transistors. If you don't have that, you can't build models that are good in semis. So what we've done is we've invested since day one in building the largest data moat in the semiconductor industry and use those data stacks that we've created in training bespoke models for chip design. So it is physics-informed because the moment you start to say, I want to do X, Y, and Z, it understands how that's going to translate into transistors on a chip. And that is the magic.
John Furrier
>> It's like having a team of engineers on staff.
Faraj Aalaei
>> Absolutely.
John Furrier
>> That's what basically it turns into.
Faraj Aalaei
>> Team of engineers who are expert across the semiconductor field, across all various applications at your fingertips. So it's really a digital co-designer.
John Furrier
>> All right, let's get into the news. You guys are launching the Artificial Chip Intelligence, ACI Enterprise, world's first full-stack intelligence infrastructure for chip design. That's the tagline, that's the sentence. Take us through the product, what your plans are, target customer, is it a designer? we're hearing OpenAI's designing their own chip, Anthropic's building their own chip. Everyone wants to build their own chips. I'm like, well, good luck with that. But now—
Faraj Aalaei
>> And we're here to serve all of them.
John Furrier
>> Take us through the value proposition, the product opportunity for you guys.
Faraj Aalaei
>> Sure. So artificial chip intelligence, ACI, is really— if you think about AGI, we are ACI, which means we're focused on the chip and chip intelligence. And the way we bring that to bear is that we combine that with very detailed workflows on top of these models. And the customer can start by saying, I have this idea, or I have this specific IP, I want to build a chip. And we take them through all the steps that the industry has been going through for 30 years, but we do it at blazing speed. We do it at 10x the speed at minimum. And so what does it do? It allows you to code in chip language. It allows you to synthesize this, simulate it, synthesize it, and create something that meets your requirements for power, performance, and area. And that's never been done before. And you can do this at software speed, really.
John Furrier
>> You know, there's an old expression in the industry, we've said it many times on theCUBE, it's quoted most by Andy Jassy, who's the CEO of Amazon. He says, "There's no compression algorithm for experience." Actually, there is. You guys are doing it. You guys have this specialism with the semis. Specialized intelligence is the hottest area around. General intelligence is great, I want to know what's going on in the world. The frontier models crawl the internet. They know all the general answers. So AGI, whatever.
Faraj Aalaei
>> That's right.
John Furrier
>> But now specialized intelligence. Talk about the impact because you guys are essentially doing that and you have specialized intelligence on the chip, chip design, all the process management, process experience. You guys have compressed that.
Faraj Aalaei
>> Yeah, we have compressed that. So think about it this way, right? Today, if you want to design a chip, it's going to take you hundreds, if not thousands of engineers, depending on how complicated the chip is, and it's going to cost you several hundred million dollars to build a chip. What we've set out to do is to change that entire game, change the industry in that way, in that you don't need thousands of engineers to design a chip. You need very few, but you need an intelligent AI enterprise system and you will arrive at the same chip, perhaps more bespoke and perhaps even better performance. At a fraction of the time. This changes our industry because now you can go from idea to shipping product at close to the speed that people develop software and take it to production. Right. And close that gap. And the more you close that gap, the more profitable semiconductor companies are going to be.
John Furrier
>> Talk about your background. It's not your first rodeo in this area. And bespoke was always kind of a— I won't say bad word, but people would roll their eyes. I'm building a bespoke system. Whoa, no, we want to have a broad market opportunity. This is a unique paradigm shift.
Faraj Aalaei
>> Yeah.
John Furrier
>> What's it like? compare it to what you've done in the past. You have multiple chip companies. Yeah.
Faraj Aalaei
>> So I've built and taken two semiconductor companies public. And what I tell you is because of the long time that it took to design chips, we pack a lot of stuff in these chips. We make them as general purpose as we can. That comes with cost, that comes with the cost in terms of time to market. It comes in terms of actually the cost of the device. And so in the world today where we have different models and different workloads, actually being able to build a general purpose or build a bespoke chip. But if you want to build a bespoke chip, you're going to have advantages in the marketplace because it is going to operate at the performance you want and doing the kinds of things you want. And not much more than that, because now if the world changes, you can react to it very quickly. It's not like the old times where I have to wait those 5 years to get a chip into production.
John Furrier
>> Yeah. And the programmability you mentioned earlier, talk about that aspect of that requirement in today's world. Yeah.
Faraj Aalaei
>> So, you can build programmable devices obviously today. FPGAs, I think, are going to be well positioned, by the way, in this marketplace. For stuff that happens at the edge in the physical AI field. And being able for us to take from idea to an FPGA program that runs hardware at speed, it can happen in a matter of hours to days. And that closes the loop and really being able to bring these products to marketplace and changing their behavior on the fly almost.
John Furrier
>> Got it. Final question for you. Now that you guys are launched, what's the focus? What's your plan?
Faraj Aalaei
>> So our plan is to expand our market reach with our customers. We're engaged with 45 customers today, and this is just the beginning of our product introduction. Our plan is to continue to make our model more and more intelligent, continue to add features and capabilities, and really make this system easy to use where you don't need to be an electrical engineer to use this system to arrive at the solution for chip design.
John Furrier
>> So congratulations and looking forward to catching up after the launch.
Faraj Aalaei
>> Thank you very much.
John Furrier
>> I'm John Furrier with theCUBE. AI factories, again, the innovation, the engineers will go to the constraints and one of them is small form factors, high performance edge robotics. Starting to see a lot more activity. More power is packed into the system when you have the smaller, faster programmable devices. Again, another example of the innovation here on theCUBE. Thanks for watching.
John Furrier