Baidu’s New Yardstick for Deep Learning Hardware Makers
When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. …
When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. …
What is good for the simulation and the machine learning is, as it turns out, also good for the database. …
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. …
There is no workload in the datacenter that can’t, in theory and in practice, be supplied as a service from a public cloud. …
While much of the work at Baidu we have focused on this year has centered on the Chinese search giant’s deep learning initiatives, many other critical, albeit less bleeding edge applications present true big data challenges. …
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. …
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