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Actionable news in INTC: Intel Corporation,

What Did Intel Just Buy?

Summary

Intel just acquired Nervana Systems, a deep learning startup, for $350-400 million.

I take a look at Nervana Systems products and its expertise in this niche.

Intel likely concerned about custom deep learning hardware by tech companies on the one hand and GPUs on the other hand.

M&A in the machine learning market keeps happening at a strong pace. Just last week, I commented on Apple's (NASDAQ:AAPL) acquisition of Turi for $200 million. This week, Intel (NASDAQ:INTC) bought Nervana Systems for reportedly over $350 million. Again, this is a private acquisition due to the nature of the deep learning market. Most companies simply never make it to an IPO before being acquired, reflecting the explosive demand for expertise in this market. What did Intel just buy?

Nervana Systems: Focus on performance

Nervana Systems: Focus on performance

Software. On the surface, Nervana Systems might seem quite similar to Apple's recent acquisition Turi. Being founded in 2014 and having grown to just short of 50 employees since then, it's a typical startup success story. Its CEO, Naveen Rao, has previously worked as a researcher for Qualcomm researching neuromorphic hardware. The company markets a number of what I would, in 2016, call standard deep learning services via its software platform, Nervana Neon.

As a disclaimer, I use deep learning frameworks for research, but I have not used Neon before. Even though it reports great performance numbers, it's not quite as established as other frameworks. The mainstream right now would be Google's (NASDAQ:GOOG) (NASDAQ:GOOGL) TensorFlow, Theano, Caffe and Torch, often used through a wrapper library like Keras. The truth is that absolute benchmark performance does not matter as much as software developers would like it to. Commercializing deep learning is more about providing the right tools and infrastructure around a framework that enables developers to smoothly incorporate them into their software stacks and applications. For instance, deeplearning4j is much slower than most of its competitors but runs on the Java Virtual machine, thus integrating nicely with most open source data processing frameworks (Hadoop/Spark). This matters more to decision makers and software architects than pure...


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