The Three Great Lies of Cloud Computing
It’s elastic! It’s on-demand! It scales dynamically to meet your needs! …
It’s elastic! It’s on-demand! It scales dynamically to meet your needs! …
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. …
The jury is still out when it comes to how wide-ranging the application set and market potential for quantum computing will be. …
While containers are old news in enterprise circles, by and large, high performance computing centers have just recently begun to consider packaging up their complex applications. …
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. …
In the last couple of years, we have examined how deep learning shops are thinking about hardware. …
We have profiled a number of processor updates and novel architectures this week in the wake of the Hot Chips conference this week, many of which have focused on clever FPGA implementations, specialized ASICs, or additions to well-known architectures, including Power and ARM. …
For those interested in novel architectures for large-scale datacenters and complex computing domains, this year has offered plenty of fodder for exploration. …
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