Custom Hardware Sharpens Edge for Deep Learning Future

February 1, 2016 Nicole Hemsoth In an era of commodity hardware and open source software—or generalization in one arena and extraordinary opportunities for customization in the other—one could argue that the playing field for specialization of new platforms has already been set. However, in some emerging areas, including wider deployments of deep learning beyond Google, Baidu, Facebook, and others, that movement could be upended by an increasing focus on configurability, customization, and fine-tuning of both hardware and software for very specific end goals. There are instances of computing systems being customized from the top down—and to rather great success, even if that success is rooted and only well known in certain circles. For example, consider the Anton supercomputer—a beast designed specifically to meet the complex tuning requirements of molecular dynamics…

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