NVIDIA Poised To Lead Computing's Next Tectonic Shift

According to Jefferies, a global investment banking firm, the next "tectonic shift" in computing is coming in fast and NVIDIA is in a prime position to lead the charge. Of course, the shift Jefferies refers to is tied directly to the rapidly growing fields associated with machine learning - namely deep learning and artificial intelligence (A.I.). What puts NVIDIA in a unique position, however, may come as a surprise to those less versed in how computer components work. It all comes down to the platform and experience NVIDIA has built for and with its graphics processing units (GPUs).

GPUs got their start in the 90's and differed from their central processing unit (CPU) counterparts in that they are capable of processing multiple problems in tandem, as opposed to the linear way in which the latter processes data. NVIDIA got into that game early and rapidly developed its own programming platform for use with that parallel processing, called CUDA. That's where A.I. and deep learning come in. Machine learning involves a plethora of complex problems that need to be solved by a computer system in order to emulate the "thinking process." While GPUs are not well suited to solving problems in the linear, singular way that a CPU can excel at, they're perfectly suited for the problems associated with A.I. Jefferies expects "serial processing (x86) architectures" to be suppressed as machine learning takes a deeper hold in the industry, paving the way for "massively parallel processing capabilities." That's also tied in directly to mobile since connected devices are expected to "approach 10b units by 2022."

Google, in the meantime, is hard at work creating and advancing its own tensor processing unit (TPU), which is purpose-built for machine learning applications. That said, Jefferies is technically correct in asserting that GPUs are a hardware architecture which already exists and it could readily be adjusted with relative ease for use in A.I. and the associated processes. TPUs are cloud-based, in their current form, giving NVIDIA an edge for creating machine learning that potentially doesn't necessarily require web access. NVIDIA's GPUs, beyond their more well-known use in gaming PCs, have already been scaled down to fit Android tablets. Furthermore, the company has made significant headway in the autonomous auto arena, teaming up with Audi, Toyota, Volkswagen, and several others in creating self-driving A.I. systems. So it is in a good position to lead the next wave of computing innovation. Whether or not the company takes advantage of that position to ultimately lead the 4th tectonic shift is all that remains to be seen.

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Daniel Golightly

Senior Staff Writer
Daniel has been writing for AndroidHeadlines since 2016. As a Senior Staff Writer for the site, Daniel specializes in reviewing a diverse range of technology products and covering topics related to Chrome OS and Chromebooks. Daniel holds a Bachelor’s Degree in Software Engineering and has a background in Writing and Graphics Design that drives his passion for Android, Google products, the science behind the technology, and the direction it's heading. Contact him at [email protected]