Intel Labs has developed a neuromorphic processor that researchers there believe can perform machine learning faster and more efficiently than that of conventional architectures like GPUs or CPUs. The new chip, codenamed Loihi, has been six years in the making.
Moore’s Law, the engine that has driven the electronics industry for the past 50 years, is running on fumes. But DARPA, the US Defense Advanced Project Agency, is looking to refill the gas tank with new research initiatives, backed by a $216 million investment.
Hyperion Research says 2016 was a banner year for sales of HPC servers. According to the analyst firm, HPC system sales reached $11.2 billion for the year, and is expected to grow more than 6 percent annually over the next five years. But it is the emerging sub-segment of artificial intelligence that will provide the highest growth rates during this period.
Over the last year, the greenest supercomputers in the world more than doubled their energy efficiency – the biggest jump since the Green500 started ranking these systems more than a decade ago. If such a pace can be maintained, exascale supercomputers operating at less than 20 MW will be possible in as little as two years. But that’s a big if.
If there was any question that machine learning would spawn chip designs aimed specifically at those applications, those doubts were laid to rest this year. The last 12 months have seen a veritable of explosion of silicon built for this new application space.
At the Hot Chips conference this week, Microsoft has revealed its latest deep learning acceleration platform, known as Project Brainwave, which the company claims can deliver “real-time AI.” The new platform uses Intel's latest Stratix 10 FPGAs.
While AI is poised to sweep through major sectors of the economy over the next decade, perhaps no industry should be more welcoming to this technology than that of healthcare. And given that the US is the most technologically advanced nation in the world, and the one with the most expensive healthcare, the country could end up being the proving ground for AI-powered medicine.
Microsoft has bought Cycle Computing, an established provider of cloud orchestration tools for high performance computing users. The acquisition offers the prospect of tighter integration between Microsoft Azure’s infrastructure and Cycle’s software, but suggests an uncertain future for the technology on Amazon Web Services (AWS) and Google’s cloud platform.
One of the biggest impediments to more widespread use of AI is the lack of developer expertise in machine learning software. Bonsai, a startup based in Berkeley, California, is looking to change that in a big way by offering a platform that abstracts away a lot of the low-level nuts and bolts that makes machine learning such a daunting challenge for businesses.
This week IBM demonstrated software that was able to significantly boost the speed of training deep neural networks, while improving the accuracy of those networks. The software achieved this by dramatically increasing the scalability of these training applications across large number of GPUs.