If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

At Intel's recent AI Day, the chipmaker previewed a series of future products that it intends to use to unseat GPUs as the de facto standard for machine learning. The one-day event was Intel's most assertive pronouncement of its intentions to become a major player in the artificial intelligence market. Perhaps the most surprising element of the roadmap is the productization of the Nervana Engine, the machine learning chip that Nervana engineers were working on prior to Intel's acquisition of the company earlier this year. Given Intel's devotion to Xeon Phi, we at TOP500 News were more than a little skeptical that the company would ever commercialize the Nervana designs. But that is apparently the plan.

In Supercomputing Conference (SC) years past, chipmaker Intel has always come forth with a strong story, either as an enabling processor or co-processor force, or more recently, as a prime contractor for a leading-class national lab supercomputer. But outside of a few announcements at this year's SC related to beefed-up SKUs for high performance computing and Skylake plans, the real emphasis back in Portland seemed to ring far fainter for HPC and much louder for the newest server tech darlings, deep learning and machine learning. Far from the HPC crowd last week was Intel's AI Day, an event in San Francisco chock full of announcements on both the hardware and software fronts during a week that has historically emphasized Intel's revolving efforts in supercomputing. As we have noted before, there is a great deal of overlap between these two segments, so it is not fair to suggest that Intel is ditching one community for the other. In fact, it is quite the opposite--or more specifically, these areas are merging to a greater degree (and far faster) than most could have anticipated.

Intel is taking a new direction in chip development as it looks to the future of artificial intelligence, with the company betting the technology will pervade applications and web services. The company on Thursday said it is developing new chips that will handle AI workloads, which will increasingly be a part of its chip future. For now, the AI chips will be released as specialized primary chips or co-processors in computers and separate from the major product lines. But over time, Intel could adapt and integrate the AI features into its mainstream server, IoT, and perhaps even PC chips. The AI features could be useful in servers, drones, robots, and autonomous cars.

Intel is taking a new direction in chip development as it looks to the future of artificial intelligence, with the company betting the technology will pervade applications and web services. The company on Thursday said it is developing new chips that will handle AI workloads, which will increasingly be a part of its chip future. For now, the AI chips will be released as specialized primary chips or co-processors in computers and separate from the major product lines. But over time, Intel could adapt and integrate the AI features into its mainstream server, IoT, and perhaps even PC chips. The AI features could be useful in servers, drones, robots, and autonomous cars.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

At the Intel Developer Forum in San Francisco this week, Intel Senior Vice President and General Manager Diane Bryant announced the launch of Intel's Silicon Photonics product line and teased a brand-new Phi product, codenamed "Knights Mill," aimed at machine learning workloads. With the introduction of Silicon Photonics, Intel is debuting two new 100G optical transceivers. Sixteen years in the making, the small form-factor design fuses optical components with silicon integrated circuits to provide 100 gigabits per second over a distance of two kilometers. Initial target applications include connectivity for cloud and enterprise datacenters as well as Ethernet switch, router, and client-side telecom interfaces. Microsoft is adopting the technology for its scale-loving Azure datacenters.

Artificial intelligence can find, map poverty, researchers ...

Intel has pulled open the curtain on a secretly developed mega-chip called Knights Mill, a key component in its artificial-intelligence strategy. The chip -- which belongs to the family of high-performance Xeon Phi processors -- gives Intel a legitimate opportunity to tackle machine learning. It is targeted at servers and workstations, and will be available in 2017. Intel was caught off-guard with the emergence of artificial intelligence as a way to analyze and present data. Knights Mill, introduced on Wednesday at the ongoing Intel Developer Forum, will fill a big hole in company's chip lineup.