13 DAILY CVPR Sunday Shalini DeMello Yes, yes. Software is as much important as hardware for NVIDIA. Is that right? Oh, absolutely. Software and algorithms in particular. And that's exactly the charter of my team, which is to design new algorithms. How did you find yourself there? That's an interesting question. During my PhD, I worked on 3D face recognition. This was back in 2008. Computer vision was always what I was interested in. So my first job after I finished my PhD was more as an imaging scientist where I was helping to design algorithms for the image signal processing pipelines. But honestly, I always loved computer vision. That's what I enjoyed. I worked at Texas Instruments for the first two years after I finished my PhD as an imaging scientist. And then I had an opportunity to get back more into computer vision, joining NVIDIA in 2011 in a computer vision role. And I've never looked back. All this in America, right? Yes, this was all in America. I want to add a very unique and interesting point to the story: Texas Instruments had this digital signal processing chip DSP at the time. And they had this computer vision library that they had created that ran efficiently on the DSP. And it was a very sad situation because it had such low compute that you had to strip out most of the goodies in your computer vision algorithms to just even make them work at all on those processors. The reason I joined NVIDIA in 2011 was because, as a computer vision researcher, I wanted to work with a company that had a lot of compute. I didn't know that GPUs would become so important, but I just knew that I didn't want my hands to be tied with compute wherever I go. And that was my biggest motivation for joining NVIDIA. It's like NVIDIA has a GPU that has a lot of power, has a lot of compute, and it will give me the freedom to innovate in the algorithmic space and be able to run hard and compute heavy algorithms. That was my motivation. And I guess the bet worked. So today I learned that also for Texas Instruments, software was as important as hardware. Right. Because at the end of the day, what is the hardware for? For running algorithms and software on it, right? It's a perfect marriage. So the way we
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