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Intel agreed to acquire San Diego deep-learning company Nervana Systems on August 9, 2016, and announced that the acquisition was complete on August 23. Intel said it wanted Nervana’s software, intellectual property, and silicon expertise to strengthen its AI portfolio and optimize deep-learning work on Intel data-center processors.
What Intel agreed to acquire
Nervana Systems, founded in 2014 and headquartered in San Diego, California, developed deep-learning software and hardware. In its August 9, 2016 announcement, Intel described Nervana as having an optimized software and hardware stack, along with intellectual property and expertise for accelerating deep-learning algorithms.
Intel framed the deal as a way to combine Nervana’s capabilities with its existing processors and software—not simply as the purchase of a standalone chip design. The company said Nervana’s expertise could help expand Intel’s AI capabilities and improve how deep-learning workloads ran across its platform.
Why Intel said Nervana fit its data-center strategy
Software and framework optimization
Intel said it intended to use Nervana’s software expertise to further optimize the Intel Math Kernel Library and its integration with industry-standard frameworks. The aim was to make Intel’s software and processors work more effectively for deep-learning workloads. That was Intel’s stated plan, not an independently verified performance result.
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Silicon and processor integration
Intel also pointed to Nervana’s Engine and silicon expertise. Diane Bryant, then Intel’s executive vice president and general manager of the Data Center Group, said: “Nervana’s Engine and silicon expertise will advance Intel’s AI portfolio and enhance the deep learning performance and TCO of our Intel Xeon and Intel Xeon Phi processors.” This was the company’s rationale for the acquisition, not evidence by itself that those improvements were achieved.
Intel’s August 23 closing announcement described the combination as bringing Intel processor engineers together with Nervana’s machine-learning experts.
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How the deal was viewed in the AI hardware race
Contemporary coverage placed the acquisition in a broader contest over hardware for AI. Bloomberg’s August 9, 2016 report characterized Nervana as bringing software, a cloud service, and future hardware, and interpreted the deal as a way for Intel to tune its products for AI workloads. Bloomberg also discussed potential competition with Nvidia as the AI hardware market was growing. That was the report’s market framing; it was not Intel’s exact description of the acquisition.
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In a November 17, 2016 strategy announcement, Intel introduced its Intel Nervana portfolio and outlined plans for Lake Crest, a neural-network product. Intel said it planned to have first silicon ready for testing in the first half of 2017, with availability to key customers later that year. Those were roadmap targets announced in 2016; they do not establish when a product ultimately shipped or what performance it achieved.
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- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
That announcement also included two figures that should be read as historical Intel claims, not current independent measurements:
- Intel set a goal of achieving “up to 100x reduction in the time to train a deep learning model over the next three years compared to GPU solutions.” This was a target, not a result demonstrated by the announcement.
- Intel said it powered 97 percent of data-center servers running AI workloads. This was Intel’s own claim in 2016, not a current or independently verified market statistic.
Was a purchase price disclosed?
Intel did not disclose the acquisition’s financial terms in its announcement. TechCrunch reported, citing Recode, that the price was above $350 million. That figure is an attributed media estimate, not a price confirmed by Intel; the sources do not establish a definitive transaction price.
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