May 17, 2020

Gartner expects $3.7tn in global IT spending during 2018

Gartner
Global IT spending
Communication services
Telecommunications
Jonathan Dyble
2 min
Global technology
Leading US research firm Gartner has predicted in a new report that global IT spending will exceed $3.7tn throughout the course of 2018 – up 4.5% comp...

Leading US research firm Gartner has predicted in a new report that global IT spending will exceed $3.7tn throughout the course of 2018 – up 4.5% compared to 2017.

“Global IT spending growth began to turn around in 2017, with continued growth expected over the next few years,” said John-David Lovelock, Research Vice President at Gartner, despite uncertainty over Brexit, currency fluctuations and the potential of global recession.

Communication services are expected to harbor the largest portion of this, with Gartner predicting the industry to exceed 1.4tn in spending through 2018.

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However, enterprise software is forecast to be the sector that see’s the highest level of growth this year, with Gartner predicting expenditure in this department to rise from $355bn to $389bn – a 9.5% annual growth.

Further, Gartner expects that spending patterns will begin to shift, as emerging technologies such as IoT, AI and new big data algorithms become incorporated more readily into businesses due to their productive potential, driving industry growth.

“Looking at some of the key areas driving spending over the next few years, Gartner forecasts $2.9 trillion in new business value opportunities attributable to AI by 2021, as well as the ability to recover 6.2 billion hours of worker productivity,” said Mr. Lovelock.

“That business value is attributable to using AI to, for example, drive efficiency gains, create insights that personalize the customer experience, entice engagement and commerce, and aid in expanding revenue-generating opportunities as part of new business models driven by the insights from data.”

Gaining access to this potential will require investment, however, ultimately this should lead to cost savings for the majority of firms who turn to AI-based applications.

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Jun 11, 2021

Google AI Designs Next-Gen Chips In Under 6 Hours

Google
AI
Manufacturing
semiconductor
3 min
Google AI’s deep reinforcement learning algorithms can optimise chip floor plans exponentially faster than their human counterparts

In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product. 

 

“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates

 

Training the Algorithm 

In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own. 

 

Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could. 

 

As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks. 

 

Major Implications for the Semiconductor Sector 

Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.

 

Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”. 

 

All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society. 

 

 

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