Sep 16, 2020

Baidu demonstrates automated vehicles and 5G remote driving

William Smith
2 min
Chinese technology company Baidu has demonstrated what it calls “Fully Automated Driving” with its Apollo platform and a Weltmeister EV
Chinese technology company Baidu has demonstrated what it calls “Fully Automated Driving” with its Apollo platform and a Weltmeister EV...

Chinese technology company Baidu has demonstrated what it calls “Fully Automated Driving” with its Apollo platform and a Weltmeister EV.

Baidu said the system is now capable of driving without a safety driver inside the vehicle, making wider deployment a possibility.

The demonstration took place at Baidu World 2020, an annual technology conference operated by the company. Baidu’s solution involves adding the system to pre-installed and mass-produced vehicles.

The Apollo platform has now completed over six million kilometres of on-road testing, experiencing zero accidents while carrying over 100,000 passengers across 27 cities.

The platform also makes use of 5G communications technology to enable remote driving - replacing the safety driver typically ready to assume control of autonomous vehicles in cases of emergency. Baidu said its remote operators have all completed over 1,000 hours of cloud-based driving training, and are ready to assume control in an emergency.

The company stated its autonomous valet parking technology was ranked level 4 on the Society of Automotive Engineers (SAE) Levels of Driving Automation Standard, with the prized level 5 meaning a vehicle able to operate in all conditions without human interaction.

Baidu predicted that the autonomous driving industry would be fully commercialised in 2025.

China has emerged as a leader in autonomous vehicle development, alongside the United States. One of its premier competitors is ride-hailing firm DiDi, which previously bought out Uber’s Chinese business, and is currently testing its solutions on public roads in cities such as Shanghai.

The company has said it plans to have more than one million robotaxis (self-driving vehicles in public use) by 2030, operating in areas where human ride-hailing drivers are less available. Elon Musk’s Tesla had targeted a million-strong robotaxi network this year, by pushing a software update to its existing vehicles, but that now seems unlikely.

(Image: Baidu)

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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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