We have written much about large-scale deep learning implementations over the last couple of years, but one question that is being posed with increasing frequency is how these workloads (training in particular) will scale to many nodes. …
Over the last two years, there has been a push for novel architectures to feed the needs of machine learning and more specifically, deep neural networks. …
Hardware and device makers are in a mad dash to create or acquire the perfect chip for performing deep learning training and inference. …
Over the last couple of years, we have focused extensively on the hardware required for training deep neural networks and other machine learning algorithms. …
Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. …
Over the last year in particular, we have documented the merger between high performance computing and deep learning and its various shared hardware and software ties. …
There is little doubt that 2017 will be a dense year for deep learning. …
Deep learning and machine learning are major themes at this year’s annual Supercomputing Conference (SC16), both in terms of vendors showcasing systems that are a fit for both high performance computing and machine learning, and in the revelation of new efforts to combine traditional simulations with neural networks for greater efficiency and insight. …
Earlier this month, Samsung acquired Viv, the AI platform built by the creators of Siri that seeks to “open up the world of AI assistants to all developers. …
When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. …
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