The ability of the human brain to process massive amounts of information while consuming minimal energy has long fascinated scientists. When there is a need, the brain dials up computation, but then it rapidly reverts to a baseline state. Within the realm of silicon-based computing, such efficiencies have never been possible. Processing large volumes of data requires massive amounts of electrical energy. Moreover, when artificial intelligence (AI) and its cousins deep learning and machine learning enter the picture, the problem grows exponentially worse.Emerging neuromorphic chip designs may change all of this. The concept of a brain-like computing architecture, conceived in the late 1980s by California Institute of Technology professor Carver Mead, is suddenly taking shape. Neuromorphic frameworks incorporate radically different chip designs and algorithms to mimic the way the human brain works—while consuming only a fraction of the energy of today's microprocessors. The computing model takes direct aim at the inefficiencies of existing computing frameworks—namely the von Neumann bottleneck—which forces a processor to remain idle while it waits for data to move to and from memory and other components. This causes slow-downs and limits more advanced uses. "Neuromorphic chips introduce a level of parallelism that doesn't exist in today's hardware, including GPUs and most AI accelerators," says Chris Eliasmith, a professor in the departments of Systems Design Engineering, and Philosophy, of the University of Waterloo in Ontario, Canada...
The impact of commercial neuromorphic computing could be enormous. The technology has repercussions across a wide swath of fields, including image and speech recognition, robotics and autonomous vehicles, sensors running in the Internet of Things (IoT), medical devices, and even artificial body parts.As Adam Stieg, associate director of the California NanoSystems Institute at the University of California at Los Angeles (UCLA) puts it: "The ability to perform computation and learning on the device itself, combined with ultra-low energy consumption, could dramatically change the landscape of modern computing technology."
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