May 17, 2020

Global IoT spending could reach $15tn between 2017-2025

IoT
Internet of Things
Report
market
Jonathan Dyble
2 min
IoT
A new report from Business Insider Intelligence (BI Intelligence), has forecast that in the period between 2017-2025, total internet of things (IoT) inv...

A new report from Business Insider Intelligence (BI Intelligence), has forecast that in the period between 2017-2025, total internet of things (IoT) investments could reach up to $15tn.

The company predicts that this will be driven by an increasing adoption of a variety of IoT devices across a number of markets, from the consumer world with smart assistants such as Amazon’s Echo range, to AI-powered data analytics platforms that specialise in complex business applications.

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The results of BI Intelligence’s second annual Global IoT Executive Survey are based on answers from over 400 key executives from organisations across the world.

Alongside the $15tn that is forecast to be spent, the firm expects that there will be more than 55 billion IoT devices across the globe by the end of the period in question, up from the nine billion that was recorded in 2017, as more companies plan to invest heavily in IoT solutions.

“The report highlights the opinions and experiences of IoT decision-makers on topics that include: drivers for adoption; major challenges and pain points; deployment and maturity of IoT implementations; investment in and utilization of devices; the decision-making process; and forward- looking plans,” BI Intelligence said.

For more information, see the full Global IoT Executive Survey report.

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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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