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It’s not just a deteriorating Moore’s Law. The other driver toward specialized processors is a new set of applications that are not amenable to general-purpose computing. For starters, you have platforms like mobile devices and the internet of things (IoT) that are so demanding with regard to energy efficiency and cost, and are deployed in such large volumes, that they necessitated customized chips even with a relatively robust Moore’s Law in place. Lower-volume applications with even more stringent requirements, such as in military and aviation hardware, are also conducive to special-purpose designs. But the authors believe the real watershed moment for the industry is being enabled by deep learning, an application category that cuts across nearly every computing environment – mobile, desktop, embedded, cloud, and supercomputing.
Deep learning and its preferred hardware platform, GPUs, represent the most visible example of how computing may travel down the path from general-purpose to specialized processors.
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