The Battle of AI Processors Begins in 2018

2018 will be the start of what could be a longstanding battle between chipmakers to determine who creates the hardware that artificial intelligence lives on.

2018 Look AheadThis article is part of Design News’ 2018 Look Ahead package, offering perspective and insight on 10 areas of advancing engineering. Before you dive too deeply into 2018, prepare yourself for what will surely be an innovative new year with Design News’ 2018 Look Ahead articles.

CPUs, GPUs, TPUs (tensor processing units), and even FPGAs. It's hard to tell who started the fight over artificial intelligence (AI), and it's too soon to tell who will finish it. But 2018 will be the start of what could be a longstanding battle between chipmakers to determine who creates the hardware that AI lives on.

When Intel made its latest AI hardware announcement at Automobility LA 2017 it wasn't a gauntlet being laid. Instead it was only the latest in a series one-upmanships on the part of a handful of major tech giants – all seeking to lay their claim in the AI hardware landscape. Because whomever rules AI will stand to become one of, if not the, dominate force in many industries including manufacturing, automotive, IoT, medical, and even entertainment.

On the hardware front, artificial intelligence is locked into its own personal Game of Thrones, with different houses all vying for supremacy and to create the chip architecture that will become the standard for AI technologies – particularly deep learning and neural networks.

Analysts at both Research and Markets and TechNavio have predicted the global AI chip market to grow at a compound annual growth rate of about 54% between 2017 and 2021.

Raghu Raj Singh, a lead analyst for embedded systems research at Technavio, said the need for high-power hardware that can handle the demands of deep learning are a key driver to this growth. “The high growth rate of hardware is due to the increasing need for hardware platforms with high computing power, which helps run algorithms for deep learning. The growing competition between startups and established players is leading the development of new AI products, both for hardware and software platforms that run deep learning programs and algorithms.”

Competition is heating up. AI is set to be the next big frontier for computing hardware and may be the most significant battleground for computer hardware since the emergence of mobile computing and the Internet.

So, how did we get here and who are the big players?

Good 'ol CPUs

When level 5 autonomous cars, those that require no human intervention to operate, hit the roads they will be among the smartest and most sophisticated machines ever created. Naturally self-driving vehicles have become one of the main targets for AI, and its where chipmaker Intel is looking to firmly stake its claim.

Rather than conducting all of its R&D in-house, Intel has used acquisitions to establish itself in the AI landscape. In August of 2016 Intel purchased Nervana Systems, a maker of neural network processors.

Neural networks are capable of performing a wide range of tasks very efficiently, but in order to do these tasks a network first has to be taught how to do them. A neural network to perform something as basic as recognizing pictures of dogs has to first be taught what a dog looks like, ideally across all breeds. This can mean exposing the network to thousands, if not millions, of images of dogs – a task that can be enormously time consuming without enough processing power.

In November of 2016, mere months after acquiring Nervana, Intel announced a new line of processors – the Nervana platform – targeted directly at AI-related applications such as training neural networks. “We expect the Intel Nervana platform to produce breakthrough performance and dramatic reductions in the time to train complex neural networks,” Diane Bryant, executive vice president and general manager of the Data Center Group at Intel said. “Before the end of the decade, Intel will deliver a 100-fold increase in performance that will turbocharge the pace of innovation in the emerging deep learning space.”

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