In a tweet on Friday, deep learning analyst Karl Freund announced that Intel would "close the door" on Nervana, the deep learning chip startup Intel acquired in 2016, and instead focus on Habana Labs, the other startup that Intel acquired in December for almost $2 billion. Intel informed Freund of its new AI strategy going forward. Intel will support the NNP-I inference chip "for previously committed customers," but says that it will completely cease development of the NNP-T AI training design. Intel stopping development of the NNP-T doesn't come as a complete surprise, given the acquisition of Habana in December: both companies make chips targeted at artificial intelligence workloads in the data center (deep neural networks). At the time of the acquisition, there was already much speculation about what this implied for Nervana.

For the last few years, Intel (INTC) has been shifting its focus away from PC to data-centric businesses. It's looking to tap future technologies such as AI, autonomous vehicles, and 5G networking infrastructure. NVIDIA (NVDA) is a leader in the AI space. Intel has identified NVIDIA as its AI competitor, as data centers prefer the latter's Tesla GPUs (graphics processing unit) for their AI workloads. Intel has tried to compete with NVIDIA's Tesla GPUs with its Altera field-programmable gate arrays, Xeon Phi processors, and traditional Core processors.

Intel revealed the broad outlines of its new Nervana Neural Network Processor for Inference, of NNP-I for short, that comes as a modified 10nm Ice Lake processor that will ride on a PCB that slots into an M.2 port (yes, an M.2 port that is normally used for storage), at an event in Haifa, Israel two months ago. Today, the company provided further deep-dive details of the design here at Hot Chips 31, the premier venue for leading semiconductor vendors to detail their latest microarchitectures. Intel is working on several different initiatives to increase its presence in the booming AI market with its'AI everywhere' strategy. The company's broad approach includes GPUs, FPGAs, and custom ASICs to all tackle different challenges in the AI space, with some solutions designed for compute-intensive training tasks that create complex neural networks for object recognition, speech translation, and voice synthesis workloads, to name a few, and separate solutions for running the resulting trained models as lightweight code in a process called inference. Intel's Spring Hill Nervana Neural Network Processor for inference (NNP-I) 1000, which we'll refer to as the NNP-I, tackles those lightweight inference workloads in the data center.

In case you've been living under a rock, artificial intelligence (AI) has become one of the biggest buzz phrases in technology. Unsurprisingly, then, many computer-chip companies have been rushing to build chips that can quickly and efficiently handle artificial-intelligence workloads. One of those companies is chip giant Intel (NASDAQ:INTC). At CES 2019, Intel talked up a new chip that it's building, called the Neural Network Processor for Inference, or NNP-I for short. According to the company, "[This] new class of chip is dedicated to accelerating inference for companies with high workload demands and is expected to go into production this year."