When introducing its monster 72-core Xeon Phi chip, Intel couldn't help but take a swipe at graphics processors for being sluggish for some tasks. Ironically, Xeon Phi is a byproduct of Larrabee, which was supposed to be Intel's first major GPU but was abandoned in 2009 after multiple delays. The swipe was a shot at Nvidia, whose GPUs are flourishing in the gaming and machine learning areas. But Nvidia's success has also raised questions about whether Intel should've been patient and pursued Larrabee. Nevertheless, Xeon Phi has been successfully used in supercomputing, and now Intel wants to challenge Nvidia's GPU by bringing the chip to machine learning.

Intel today unveiled new hardware and software targeting the artificial intelligence (AI) market, which has emerged as a focus of investment for the largest data center operators. The chipmaker introduced an FPGA accelerator that offers more horsepower for companies developing new AI-powered services. The Intel Deep Learning Inference Accelerator (DLIA) combines traditional Intel CPUs with field programmable gate arrays (FPGAs), semiconductors that can be reprogrammed to perform specialized computing tasks. FPGAs allow users to tailor compute power to specific workloads or applications. The DLIA is the first hardware product emerging from Intel's $16 billion acquisition of Altera last year.

Intel's chip arsenal appears to have some glaring weaknesses. One of them is the lack of a high-end graphics processor, which is important for gaming, virtual reality and machine learning. However, the company does have powerful alternatives: two monster chips that will be ammunition to take on GPUs and rival chips in the areas of machine learning and supercomputing, which are important to the company. In 2018, Intel will likely release a faster and more power-efficient Xeon Phi, a supercomputing chip that is already used in some of the world's fastest computers. Intel is also looking beyond CPUs to FPGAs (field programmable gate arrays), which can be faster at key tasks.

From ISC 2016 in Frankfurt, Germany, this week, Intel Corp. launched the second-generation Xeon Phi product family, formerly code-named Knights Landing, aimed at HPC and machine learning workloads. The company had been shipping "Knights Landing" silicon to early customers for the last six months and was waiting to ramp up production before making the product generally available. The window also gave OEMs time to complete their readiness, said Intel's Charlie Wuischpard, vice president of the Data Center Group and general manager of High Performance Computing Platform Group, in a media pre-briefing. Those OEMs include the usual names: Cray, HPE, Lenovo, Dell and others. The most distinguishing feature of the chip is that it's a bootable host CPU -- unlike its predecessor "Knights Corner," which is a coprocessor that connects over PCIe.

Intel's chip arsenal appears to have some glaring weaknesses. One of them is the lack of a high-end graphics processor, which is important for gaming, virtual reality and machine learning. However, the company does have powerful alternatives: two monster chips that will be ammunition to take on GPUs and rival chips in the areas of machine learning and supercomputing, which are important to the company. In 2018, Intel will likely release a faster and more power-efficient Xeon Phi, a supercomputing chip that is already used in some of the world's fastest computers. Intel is also looking beyond CPUs to FPGAs (field programmable gate arrays), which can be faster at key tasks.

Intel's strategy for tackling the AI CPU market, where it is facing competition from leading GPU makers and potentially also big customers that make their own specialized processors for this purpose, such as Google, rests to a great extent on designing systems that scale out rather than up. The latter, according to the chipmaker, is the conventional but inefficient approach to architecting these systems. Software code in today's machine learning systems (machine learning is one of the most active subfields in the development of artificial intelligence) is tough to scale and usually lives in a single box, Charles Wuischpard, VP of the Intel Data Center Group and general manager of the giant's HPC Platform Group, said. Companies generally buy high-power scale-up systems filled with GPUs. "In a way, there's an efficiency loss here," he said on a call with reporters last week.

When introducing its monster 72-core Xeon Phi chip, Intel couldn't help but take a swipe at graphics processors for being sluggish for some tasks. Ironically, Xeon Phi is a byproduct of Larrabee, which was supposed to be Intel's first major GPU but was abandoned in 2009 after multiple delays. The swipe was a shot at Nvidia, whose GPUs are flourishing in the gaming and machine learning areas. But Nvidia's success has also raised questions about whether Intel should've been patient and pursued Larrabee. Nevertheless, Xeon Phi has been successfully used in supercomputing, and now Intel wants to challenge Nvidia's GPU by bringing the chip to machine learning.