Last week we described the next stage of deep learning hardware developments in some detail, focusing on a few specific architectures that capture what the rapidly-evolving field of machine learning algorithms require. …
No one knows for sure how pervasive deep learning and artificial intelligence are in the aggregate across all of the datacenters in the world, but what we do know is that the use of these techniques is growing and could represent a big chunk of the processing that gets done every millisecond of every day. …
We have heard about a great number of new architectures and approaches to scalable and efficient deep learning processing that sit outside of the standard CPU, GPU, and FPGA box and while each is different, many are leveraging a common element at all-important memory layer. …
Over the long course of IT history, the burden has been on the software side to keep pace with rapid hardware advances—to exploit new capabilities and boldly go where no benchmarks have gone before. …
Intel has planted some solid stakes in the ground for the future of deep learning over the last month with its acquisition of deep learning chip startup, Nervana Systems, and most recently, mobile and embedded machine learning company, Movidius. …
Over the last couple of years, the idea that the most efficient and high performance way to accelerate deep learning training and inference is with a custom ASIC—something designed to fit the specific needs of modern frameworks. …
Following yesterday’s acquisition of deep learning chip startup Nervana Systems by Intel, we talked with the company’s CEO, Naveen Rao, about what plans are for both the forthcoming hardware and internally developed Neon software stack now that the technology is under a much broader umbrella. …
Update – 8/9/16 1:00 p.m. Pacific – Not even 24 hours after this story was posted Intel bought Nervana Systems. …
In the last couple of years, we have written and heard about the usefulness of GPUs for deep learning training as well as, to a lesser extent, custom ASICs and FPGAs. …
As we have written about extensively here at The Next Platform, there is no shortage of use cases in deep learning and machine learning where HPC hardware and software approaches have bled over to power next generation applications in image, speech, video, and other classification and learning tasks. …
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