Hyundai Mobis to launch autonomous driving sensors by 2020
Hyundai Mobis, the parts and services division of the world’s third largest vehicle manufacturer, has outlined its plans to become a leader in innovative vehicle technologies.
The company has set a goal of releasing fully operational autonomous driving sensors, pivotal to the successful operation of autonomous vehicles, by 2020.
In the aim of achieving this, the company has announced that it will both increase its investment into research and development (R&D), whilst also attributing 50% of these funds to ICT research in order to drive technological development.
“We are aggressively investing in autonomous driving technologies,” said Yang Seung-wook, Executive Vice President of ICT R&D Center at Hyundai Mobis.
“We will also increase our R&D workforce for autonomous driving from the current level of 600 to over 1,000 by 2021 and increase the number of our global autonomous test vehicles from three to 20 by next year.”
In pursuit of developing the sensors, Hyundai Mobis is already working with German radar developers SMS and ASTYX. Leveraging these partnerships, the firm hopes to develop and deploy five radars for autonomous vehicles that will provide 360-degree detection, forecast to begin production by 2021.
“We are opening ourselves up to collaboration with global companies possessing innovative technologies in various ways including technical partnerships and M&A to develop cameras and lidars,” stated Gregory Baratoff, Vice President of Autonomous Driving Technologies at Hyundai Mobis.
“We aim to secure technological power to help us capture the market based on our proprietary sensors and win contracts with global automakers for supplying sensors and systems for autonomous driving.”
The sensor market is expected to reach $20.8bn by 2021.
Google AI Designs Next-Gen Chips In Under 6 Hours
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.