AI's insatiable appetite for silicon requires new chips

Don't make data science and AI engineering teams beg you for infrastructure, or your enterprise will fall behind.

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One breakthrough of AI is deep learning: a branch of machine learning that can uncannily identify objects in images, recognize voices, and create other predictive models by analyzing data. Deep learning can use regular CPUs, but for serious projects, data science and AI engineering teams must use AI chips such as GPUs that can handle massively parallel workloads to more quickly train and continuously retrain models on large data sets.

See: Next-generation digital assistants: How AI will democratize helpers for the elite

That's Why AI Infrastructure Is Necessary

It's why all the internet giants -- including Amazon, Facebook, Google, and Microsoft -- have massive investments in AI infrastructure. Forrester defines AI infrastructure as:

Integrated circuits, computer systems, and/or cloud services that are designed to optimize the performance of AI workloads, such as deep learning model training and inferencing.

See: Google Research becomes Google AI to reflect AI-first ambitions

GPUs Got This AI Chip Party Started, But The Landscape Is More Diverse

When it comes to AI deep learning, GPUs get all the press since they are readily available and dramatically reduce the time necessary to train models. Model training that took days on CPU systems takes hours on GPU systems. But it is still early days for AI and deep learning, and a parade of new options from vendors such as Intel, public clouds, and startups are on the way.

Qualcomm launches Snapdragon 710 platform in mobile AI, neural networking push

Buy Short-Term, Think Long-Term

Remember when you got a new laptop every other year because the pace of innovation was so rapid? That's where we are with AI chips, systems, and cloud. The pace of AI infrastructure innovation is fueled by the insane growth of AI, highly competitive chip and cloud vendors, and deep learning software innovations. It doesn't mean that enterprises should wait for the dust to settle. No. Enterprises have to move forward with AI and, more importantly, make their scarce AI engineering and data science teams as productive as possible by giving them the most performant-possible infrastructure to train AI models.

Also: Nvidia researchers create AI, deep-learning system to enable robots to learn from human demonstration

Don't Make Your Data Scientists Beg

Remember the Planet of the Apes movies? The orangutans are the politicians. Chimpanzees are the scientists. Gorillas are the muscle. Well, AI infrastructures are . . . the gorillas of AI.

Enterprise leaders who wish to leverage AI to remain or become leaders in their industry must equip their AI engineering and data science teams with the best and fastest tools. That certainly means staying abreast of open source innovation and leveraging differentiated enterprise data. But it also means providing those same teams with the fastest possible AI infrastructure to accelerate the AI business innovation life cycle. Why take three days to train one iteration of a deep learning model when you could do it in one hour? The algorithms, amount of data, and number of iterations necessary to train a good model will only get more intense.

Don't make data science and AI engineering teams beg you for AI infrastructure, or your enterprise will fall behind.

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Mike Gualtieri is Forrester Vice President, Principal Analyst, and Christopher Voce is Forrester Vice President, Research Director.

To learn more about AI infrastructure options to execute on deep learning, download Forrester's report, "AI Deep Learning Workloads Demand A New Approach To Infrastructure" [subscription required].

This post originally appeared here.