Dec 15, 2020

Cadillac adopts Veoneer Night Vision thermal imaging system

Automotive
Veoneer
Data
Scott Birch
2 min
Veoneer begins production of world's most advanced automotive thermal sensing system on 2021 Cadillac Escalade, greatly increasing road safety in the dark
Veoneer begins production of world's most advanced automotive thermal sensing system on 2021 Cadillac Escalade, greatly increasing road safety in the da...

Swedish automotive technology company Veoneer, Inc. has started production of its fourth generation thermal sensing system – Night Vision.  

The Night Vision system uses thermal imaging sensors to provide an accurate forward view to drivers of up to 200 metres, significantly increasing safety.

Showcased on the 2021 Cadillac Escalade, the system uses a wider field-of-view thermal camera with four times greater resolution compared to the previous generation.

Veoneer's thermal camera is fitted into the front grille of the Escalade and the image is displayed on Cadillac's new 38-inch curved OLED screen. 

The smart system senses small differences in temperature to show objects at extended ranges. Enhanced analytics help drivers see beyond their headlights – up to 200 metres in good weather – and alerts them to potential dangers in total darkness and other low visibility conditions including fog and blinding on-coming headlights.

Compared to the third-generation system, the camera size is 50% smaller and 50% lighter enabling Veoneer to include more cameras per pallet during shipment, reducing carbon emissions during transportation and delivery to the Cadillac production line. 

Data from cameras, GPS and maps drive hands-free mode

The all-new Escalade will be the first of several models to be equipped with Veoneer's automotive industry-first thermal sensing system.

"Veoneer's fourth-generation thermal sensing system is a leap forward in the innovation of smaller automotive thermal cameras with increased resolution and improved analytics to enhance the detection of pedestrians and animals on the road," says Jan Carlson, Veoneer's Chairman, President and CEO.  

"The launch of our newest thermal sensing system and the inclusion of our localization technology on the 2021 Cadillac Escalade is an important milestone in Veoneer's journey towards safe collaborative driving."

Veoneer is the market leader for automotive thermal sensing systems, delivering to 11 customers on more than 35 vehicle models. The 2021 Escalade also offers Super Cruise as an option, claimed to be the first truly hands-free driver assistance feature. Veoneer's High Definition Map and Localization Module provides Super Cruise with stored data from sensors across the vehicle including cameras, GPS, and HD Maps in order to position the vehicle and safely perform lane changes.

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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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