So we're here at Mobile World Congress 2019. Hi, so what's the latest? Well, ML is everywhere, 5G is everywhere, and 5G and ML enabled devices are just becoming more and more real. Of course, you do see a lot of hype, but what's amazed me really is the amount of reality behind the announcements, and we're seeing more and more ML enabled machine learning enabled devices, particularly in the small microcontroller world that is just taking off massively. So, especially to do AI in microcontrollers? Yes, so we've already released the compute library for CodexM-based microcontrollers. It's called SimpsisNN, and there's been massive take-up of that, and we see that just about everybody is using that. However, about a week ago, one of the things we released was something called Helium, which is the V8.1M instruction set architecture extensions. So we're making a whole bunch of instructions head architecture extensions available on CodexM-based microcontrollers to improve machine learning processing, and it's really fantastic. We're looking at 15X improvement in up to 15X performance improvement on machine learning workloads, and about 5X improvement on more traditional DSP type workloads. And we feel that this is really going to power the revolution of machine learning-based smart endpoint devices. So, is this IP or is open source accelerating algorithms? So, two things. The software available is open source, its software, which we have seen massive numbers of downloads on. The V8.1M instructions head architecture is of course IP that we will be delivering in future releases of CodexM-based microcontrollers. And the V8M has a lot of stuff doing, which that accelerates the AI even more than regular microcontrollers. So we've added a bunch of vector extension instructions to enable the sort of complex matrix arithmetic that you see very prevalent in machine learning workloads, particularly neural network codes. And they go really an awful lot faster as a result of that. So, how's the possible to do AI in those tiny microcontrollers, and what is it for? Well, really the three main use cases we see that are absolutely taking off are the three V's. So we have vibration, we have voice, and we have vision. So, vibration, this is smart monitoring of industrial devices. Voice obviously more and more enabled devices, you know, the sort of Alexa voice assistance and things like that. And vision, of course, we perceive so much of the world through what we see, and smart devices will be doing the same thing. Now, on CodexM, we're already seeing if people implementing significant capabilities on the existing devices today. What we're doing is we're announcing some future extensions which will make those devices even more powerful in the future. Future extensions. So it's like in the next generation of microcontrollers. Next generation of CodexM based microcontrollers, yes. So the microcontrollers, those V8M is not just about the security, it's also some other stuff like AI. Security is incredibly important. As our endpoint devices get more and more powerful, then it is clearly the case that there is more and more data there that we want to secure. If you're enabling these devices access to your extremely personal information, then you will want to know that that data is being kept secure. So we believe the combination of the ARM architecture and these new extensions, together with the platform system architecture we've announced some time ago, the security manifesto, all of this together makes ARM the platform a choice for these endpoint devices. So V8M, as far as remember, is like two and a half years ago with something announced? That's about right, yeah. So how has it been the adoption so far? You're seeing CodexM microcontrollers coming out now, you're seeing the silicon coming out based on those devices, and of course there are more to come. So the M23 and M33 are very popular? Yes, yes. So just to give you, you know, throw a few numbers around just to give you some idea of this, we believe we've got about 45 billion chips, shipped containing CodexM based microcontrollers. That's an awful lot of silicon. But most of those are not V8M, that's the newest of the newest, right? Yes, what are they? I don't know the breakdown of those, so I couldn't say. But a very large proportion. V8 in the microcontrollers has been around for some time. And I was just looking at the press conferences recently with Qualcomm, and the people using the Qualcomm Snapdragon 855, they're talking about 7 trillion transistors on this market. So what's the latest with the AI in the smartphone? So pretty much all, well, actually all smartphones I know of, the premium smartphones have announced that they are including neural network accelerators in those chips. Machine learning is very much a use case that is finding a home in those new smartphones, and we're only really beginning to see those use cases take off. I mean, everyone's doing smart, you know, face unlock, smart identification, fingerprint identification, things like that. But in addition to that, we're seeing a whole bunch of use cases kick off once you have downloadable applications, which are utilizing these features. So we're seeing lots of awesome stuff, especially with the camera, but lots of awesome stuff. Is it, is it maybe true that we aren't seeing enough yet? There's potentially lots more than that. Oh, there's way more than that. It's just imagination. Absolutely. We're just limited by our imagination. But yes, if you look at the machine learning use cases applied to smart video cameras, people being able to give instructions to their video camera using voice, you know, zoom in on that guy, blur out the background, you know, that sort of thing. Huge use cases from machine learning. All right.