Over the past decade that’s proven key to a growing range of applications. GPUs perform much more work for every unit of energy than CPUs. That makes them key to supercomputers that would otherwise push past the limits of today’s electrical grids. In AI, GPUs have become key to a technology called “deep learning.” Deep learning pours vast quantities of data through neural networks, training them to perform tasks too complicated for any human coder to describe. AI and Gaming: GPU-Powered Deep Learning Comes Full Circle That deep learning capability is accelerated thanks to the inclusion of dedicated Tensor Cores in NVIDIA GPUs. Tensor Cores accelerate large matrix operations, at the heart of AI, and perform mixed-precision matrix multiply-and-accumulate calculations in a single operation. That not only speeds traditional AI tasks of all kinds, it’s now being tapped to accelerate gaming. In the automotive industry, GPUs offer many benefits. They provide unmatched image recognition capabilities, as you would expect. But they’re also key to creating self-driving vehicles able to learn from — and adapt to — a vast number of different real-world scenarios. In robotics, GPUs are key to enabling machines to perceive their environment, as you would expect. Their AI capabilities, however, have become key to machines that can learn complex tasks, such as navigating autonomously. In healthcare and life sciences, GPUs offer many benefits. They’re ideal for imaging tasks, of course. But GPU-based deep learning speeds the analysis of those images. They can crunch medical data and help turn that data, through deep learning, into new capabilities. In short, GPUs have become essential. They began by accelerating gaming and graphics. Now they’re accelerating more and more areas where computing horsepower will make a difference.
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GPUs perform much more work for every unit of energy than CPUs. That makes them key to supercomputers that would otherwise push past the limits of today’s electrical grids.
In AI, GPUs have become key to a technology called “deep learning.” Deep learning pours vast quantities of data through neural networks, training them to perform tasks too complicated for any human coder to describe.
AI and Gaming: GPU-Powered Deep Learning Comes Full Circle
That deep learning capability is accelerated thanks to the inclusion of dedicated Tensor Cores in NVIDIA GPUs. Tensor Cores accelerate large matrix operations, at the heart of AI, and perform mixed-precision matrix multiply-and-accumulate calculations in a single operation. That not only speeds traditional AI tasks of all kinds, it’s now being tapped to accelerate gaming.
In the automotive industry, GPUs offer many benefits. They provide unmatched image recognition capabilities, as you would expect. But they’re also key to creating self-driving vehicles able to learn from — and adapt to — a vast number of different real-world scenarios.
In robotics, GPUs are key to enabling machines to perceive their environment, as you would expect. Their AI capabilities, however, have become key to machines that can learn complex tasks, such as navigating autonomously.
In healthcare and life sciences, GPUs offer many benefits. They’re ideal for imaging tasks, of course. But GPU-based deep learning speeds the analysis of those images. They can crunch medical data and help turn that data, through deep learning, into new capabilities.
In short, GPUs have become essential. They began by accelerating gaming and graphics. Now they’re accelerating more and more areas where computing horsepower will make a difference.