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Neuromorphic Computers: An Alternative to the von Neumann Computer
Neuromorphic computing is a new approach to designing computer chips. The technology tries to mimic the way the synapses in the human brain work and create very large numbers of electric circuits on a chip that mimic the neuro-biological interactions in the human nervous system.
Adnan Mehonic, assistant professor at UCL London, writing in Nature, said that “brain-inspired computing needs a master plan. New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate… Different brain-inspired (‘neuromorphic’) platforms use combinations of different approaches: analog data processing, asynchronous communication, massively parallel information processing, or spiking-based information representation. These properties distinguish them from von Neumann computers.”
The self-learning neuromorphic chip market is forecast by Market Research Future to grow to nearly $3 billion by 2030.
Jean Anne Incorvia, assistant professor at the University of Texas at Austin, said that “computers that think like brains can do so much more than today’s devices. By mimicking synapses, we can teach these devices to learn on the fly, without requiring huge training methods that take up so much power.”
James Bradley Aimone, a theoretical neuroscientist at Sandia National Laboratories, said that “neuromorphic hardware can yield computational advantages relevant to many applications, not just artificial intelligence to which it’s obviously kin. Newly discovered applications range from radiation transport and molecular simulations to computational finance, biology modeling, and particle physics. There are areas in which the combination of computing speed and lower energy costs may make neuromorphic computing the ultimate desirable choice.”
