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Researchers at the University of Waterloo are developing systems that mimic the human brain to improve power efficiency and performance in autonomous vehicle (AV) technology. By building on the brain’s natural strengths, the team aims to enhance AV responsiveness, reduce power consumption, and make self-driving cars more efficient and safer.
Key Highlights:
- Waterloo’s research focuses on mimicking human brain functions to enhance artificial neural networks.
- Power efficiency improvements are crucial to reducing the battery drain of AV systems.
- Collaboration with Mercedes Benz aims to enhance AV technology with neuromorphic computing.
- The team has demonstrated up to 100 times power reduction in simulations without sacrificing accuracy.
An interdisciplinary team led by Dr. Chris Eliasmith, a professor in Systems Design Engineering and Philosophy at the University of Waterloo, is developing artificial systems inspired by human brain function to improve the performance of AV technology. The team is focusing on addressing one of the key challenges for AVs: navigating, making decisions, and reacting in real-time to changing environments without significantly draining battery power.
“The brain is by far the best autonomous system we know,” Eliasmith says. “It operates with unmatched power efficiency, using only about 20 watts. Computers are smart and AI language models like ChatGPT are very human-like, but they use at least 1000 times more power, which makes them impractical for extensive mobile use. If we can take what the brain does naturally and apply it to AVs, we can build autonomous cars that not only think faster and better but also conserve battery power, allowing for better overall performance,” Eliasmith adds.
In collaboration with Mercedes Benz, Waterloo’s Computational Neuroscience Research Group (CNRG) will leverage neuromorphic computing—a design that mimics how the human brain works—to develop more efficient and safer AV technologies. This partnership reflects Waterloo’s dedication to building meaningful industry collaborations that yield social, technological, and environmental impacts.
One of the areas of focus for the research is improving scene understanding in AVs—such as the ability to interpret human cues like body language or eye contact to predict if a pedestrian will cross the street. By enhancing the perception, prediction, and control capabilities of AV systems, the team aims to create technology that reacts more accurately to the environment, ultimately improving road safety.
“Our research using neuromorphic computers has already demonstrated a 10 to 100 times reduction in the amount of power required to do control and perception tasks without any loss of accuracy, and often with improvements,” Eliasmith says. “We believe these and other benefits, like enhanced responsiveness, will scale well to fully autonomous driving systems, making them more robust and ultimately safer, while using far less power.”
Beyond AVs, Eliasmith’s work in neuromorphic computing exemplifies Waterloo’s interdisciplinary approach to tackling complex challenges. His research also extends to other technologies, such as household devices and wearables, all designed to perform complex tasks while using minimal energy. His spin-off company, Applied Brain Research (ABR), is developing the world’s first AI chip capable of full vocabulary speech recognition at extremely low power.
“Imagine a kitchen appliance that responds to unscripted, natural voice commands to defrost a chicken or preheat the oven,” Eliasmith says. “Or conversing with any disconnected device, like a virtual reality headset, in the same way you would with a person — all using a fraction of the power required by today’s AI systems. The CNRG lab and ABR are both working on exciting applications of neuromorphic computing that will transform how we live and move about in the world. The human brain is awesome technology — why try and reinvent the wheel?”
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