Specialized AI Chips Hold Both Promise and Peril for Developers

 

AI researchers need each advantage they can get when dealing with the unprecedented computational requirements of deep learning. GPU processing power has advanced aggressively, and chips at first designed to render images have become the workhorses powering world-changing AI research and development. Many of the linear algebra routines that are necessary to make Fortnite run at 120 frames per second are now powering the neural networks at the heart of cutting-edge applications of computer vision, automated speech recognition, and natural language processing.  

 

Now, the movement toward microchip specialization is changing into an arms race. Gartner projects that specialized chip sales for AI will double to around US billion in 2019 and achieve more than billion by 2023. Nvidia’s internal projections place the market for data center GPUs (which are almost solely used to power deep learning) at billion in the same time frame. In the next five years, we are going to see significant investments in custom silicon come to fruition from Amazon, ARM, Apple, IBM, Intel, Google, Microsoft, Nvidia, Qualcomm. There are also a slew of startups in the mix. CrunchBase forecasts that AI chip companies, including Cerebras, Graphcore, Groq, Mythic AI, SambaNova Systems, and Wave Computing, have jointly raised more than billion. 

 

To be clear, specialized AI chips are both important and welcomed, as they’re catalysts for transforming cutting-edge AI research into real-world applications. And yet, the flood of new AI chips, each one faster and more specialized than the next, will also seem like a throwback to the rise of enterprise software. We can expect cut-throat sales deals and software specialization focused at locking developers into working with just one vendor. Imagine if, 15 years ago, the cloud services AWS, Azure, Box, Dropbox, and GCP all came to market within 12 to 18 months. Their mission would have been to lock in as many businesses as possible — because once you are on one platform, it is tough to switch to another. This type of end-user gold rush is about to happen in AI, with tens of billions of dollars, and priceless research, at stake. 

 

Chipmakers won’t be short on promises, and the benefits will be real. But it is important for AI developers to understand that new chips that require new architectures could make their products slower to market — even with faster performance. In most cases, AI models are not going to be portable between different chip makers. Developers are well aware of the vendor lock-in risk posed by adopting higher-level cloud APIs, but in the past, the actual compute substrate has been standardized and homogeneous. This situation is going to change dramatically in the world of AI development.

 

It is rather possibly that more than half of the chip industry’s revenue will soon be driven by AI and deep learning applications. Just as software begets more software, AI begets more AI. We have seen it many times: Companies at first focus on one problem, but in the end solve many. For example, major automakers are striving to bring autonomous cars to the road, and their cutting-edge work in deep learning and computer vision is already having a cascading effect; the research is leading to such offshoot projects as Ford’s delivery robots. As specialized AI chips come to market, the current chip giants and major cloud companies will probably strike exclusive deals or acquire top performing startups. This trend will fragment the AI market rather than unifying it. All that AI developers can do now is understand what’s about to happen and plan how they’ll weigh the benefits of a faster chip with the costs of building on new architectures.

 

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