Okay. You mentioned some of the applications. What are the kinds of industries that you're getting the most interest from?
And right now, it's, I would say, across the board. And I'll give you the spectrum, and then I'll amplify the areas where I believe that's going to be the highest traction. So we are engaged with the top five customers in robotics, top five customers in the government sector, the aerospace and defense sector, automotive, medical, transportation tech, agricultural technologies, and also a category of smart vision, that's a Venn diagram, oral app or industrial automation, right?
So visual inspection, factory flow automation. So these are all the applications, and we have as a company in the last five and a half years, engaged with the top customers in each one of these market segments. Play this out five years from now, where will we see the highest volume, highest growth?
I think clearly, automotive, just by the nature of it, is going to be a key area of focus for us and the driver. Right behind it, I estimate would be robotics. I think there is a resurgence of a lot of focus on robotics.
COVID brought a lot of the supply chain issues and the legacy architectures coming in the way of global productivity in a big way. But I also think that there's a lot of articles written up on labor shortages globally. And people really want to bring in a lot of automation into the factory floor environments.
So you're going to see a lot of AMRs or robotic environments across quite a few warehouses. There's a lot of talk around humanoid robots. This is a new area of development.
Some of these may not pan out or as with everything in life, may take much longer than anybody's estimate. But the fact that I think robotics is going to play a very large portion of our lives, that's a given.
Yeah.
With these edge applications, they work with the Cloud, right? I mean, they're not intended to work independent of the Cloud, are they?
It depends on the customer's architecture. Take, for example, R-chip. R-chip is capable of working independent of the Cloud.
So you could fundamentally train an application, and you could infer that at the edge. There is really no need for the application to be connected to the Cloud, unless you choose to do so, right? And so the application is fully capable of being entirely independent, running on its own with no connectivity.
And many applications, really if you think through it in the real world, may not have connectivity elements, and or may struggle to get connectivity. And you don't want to be dependent on the connectivity alone, right? So we built a chip that's entirely capable of being self-managed, and being out there with no need for a Cloud connectivity.
But if the customer chooses to have a Cloud connectivity, either for updating the algorithms, or to really do a nightly upload of information or analytics, we enable that also.
Yeah. Are you working with, I would assume you are with many drone manufacturers, because this sounds like what you would need on drones.
Absolutely. And so we have built a platform that is the best in class in performance and power, and the programming environment we believe is the best ease of use in the industry compared to our peers, if you will. And drones are a poster child that needs all three.
They want the highest performance, the longest flight time, lowest battery consumption. And I would say we are better poised to serve that compared to anybody else. And so absolutely, we are engaged with the market leaders on unmanned aircrafts and drones.
And we see a lot of good traction for technology in that market.
Yeah. And the processing that's taking place is computer vision and analytics and control, I guess. Are there others that I'm not thinking of?
No, I think there is also navigation.
Right.
You could argue navigation fits in the context of computer vision. So there is fundamentally a camera subsystem or some kind of a LIDAR radar subsystem. So gathering location-specific data and you're computing and you're making decisions.
So it's a combination of computer vision, perception, localization, segmentation, and really distributing the workload, both from an AI context, but also into classic computer vision. And you also have control plane functions to watch your flight navigation, and really ensuring that I think you're combining all that into a single chip. And this is kind of what we do well.
And given that we are a heterogeneous computer platform, we do more than AI workloads. And so you could actually put the control plane, the navigation, the decision-making, and have it all work on a single chip.
no subject
And right now, it's, I would say, across the board. And I'll give you the spectrum, and then I'll amplify the areas where I believe that's going to be the highest traction. So we are engaged with the top five customers in robotics, top five customers in the government sector, the aerospace and defense sector, automotive, medical, transportation tech, agricultural technologies, and also a category of smart vision, that's a Venn diagram, oral app or industrial automation, right?
So visual inspection, factory flow automation. So these are all the applications, and we have as a company in the last five and a half years, engaged with the top customers in each one of these market segments. Play this out five years from now, where will we see the highest volume, highest growth?
I think clearly, automotive, just by the nature of it, is going to be a key area of focus for us and the driver. Right behind it, I estimate would be robotics. I think there is a resurgence of a lot of focus on robotics.
COVID brought a lot of the supply chain issues and the legacy architectures coming in the way of global productivity in a big way. But I also think that there's a lot of articles written up on labor shortages globally. And people really want to bring in a lot of automation into the factory floor environments.
So you're going to see a lot of AMRs or robotic environments across quite a few warehouses. There's a lot of talk around humanoid robots. This is a new area of development.
Some of these may not pan out or as with everything in life, may take much longer than anybody's estimate. But the fact that I think robotics is going to play a very large portion of our lives, that's a given.
Yeah.
With these edge applications, they work with the Cloud, right? I mean, they're not intended to work independent of the Cloud, are they?
It depends on the customer's architecture. Take, for example, R-chip. R-chip is capable of working independent of the Cloud.
So you could fundamentally train an application, and you could infer that at the edge. There is really no need for the application to be connected to the Cloud, unless you choose to do so, right? And so the application is fully capable of being entirely independent, running on its own with no connectivity.
And many applications, really if you think through it in the real world, may not have connectivity elements, and or may struggle to get connectivity. And you don't want to be dependent on the connectivity alone, right? So we built a chip that's entirely capable of being self-managed, and being out there with no need for a Cloud connectivity.
But if the customer chooses to have a Cloud connectivity, either for updating the algorithms, or to really do a nightly upload of information or analytics, we enable that also.
Yeah. Are you working with, I would assume you are with many drone manufacturers, because this sounds like what you would need on drones.
Absolutely. And so we have built a platform that is the best in class in performance and power, and the programming environment we believe is the best ease of use in the industry compared to our peers, if you will. And drones are a poster child that needs all three.
They want the highest performance, the longest flight time, lowest battery consumption. And I would say we are better poised to serve that compared to anybody else. And so absolutely, we are engaged with the market leaders on unmanned aircrafts and drones.
And we see a lot of good traction for technology in that market.
Yeah. And the processing that's taking place is computer vision and analytics and control, I guess. Are there others that I'm not thinking of?
No, I think there is also navigation.
Right.
You could argue navigation fits in the context of computer vision. So there is fundamentally a camera subsystem or some kind of a LIDAR radar subsystem. So gathering location-specific data and you're computing and you're making decisions.
So it's a combination of computer vision, perception, localization, segmentation, and really distributing the workload, both from an AI context, but also into classic computer vision. And you also have control plane functions to watch your flight navigation, and really ensuring that I think you're combining all that into a single chip. And this is kind of what we do well.
And given that we are a heterogeneous computer platform, we do more than AI workloads. And so you could actually put the control plane, the navigation, the decision-making, and have it all work on a single chip.