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Neuromorphic Chips – Addressing the Nanotransistor Challenge by Combining Analog Computation with Digital Communication

June 27, 2018 @ 6:00 pm - 8:00 pm PDT

Speaker: Kwabena Boahen, Stanford University

Abstract: As transistors shrink to nanoscale dimensions, trapped electrons–blocking “lanes” of electron traffic–are making it difficult for digital computers to work. In stark contrast, the brain works fine with single-lane nanoscale devices that are intermittently blocked (ion channels). Conjecturing that it achieves error-tolerance by combining analog dendritic computation with digital axonal communication, neuromorphic engineers (neuromorphs) began emulating dendrites with subthreshold analog circuits and axons with asynchronous digital circuits in the mid-1980s. Three decades in, they achieved a consequential scale with Neurogrid, the first neuromorphic system with billions of synaptic connections. Neuromorphs then tackled the challenge of mapping arbitrary computations onto neuromorphic chips in a manner robust to lanes intermittently–or even permanently–blocked by trapped electrons. Having demonstrated scalability and programmability, they now seek to encode continuous signals with spike trains in a manner that promises greater energy efficiency than all-analog or all-digital computing across a five-decade precision range.

Biography: Kwabena Boahen is a Professor of Bioengineering and Electrical Engineering at Stanford University, where he directs the Brains in Silicon Lab. He is a neuromorphic engineer who is using silicon integrated circuits to emulate the way neurons compute, and linking the seemingly disparate fields of electronics and computer science with neurobiology and medicine. His lab developed Neurogrid, a specialized hardware platform created at Stanford that enables the cortex’s inner workings to be simulated in real time–something outside the reach of even the fastest supercomputers. His interest in neural nets developed soon after he left his native Ghana to pursue undergraduate studies in Electrical and Computer Engineering at Johns Hopkins University, Baltimore, in 1985. He went on to earn a doctorate in Computation and Neural Systems at the California Institute of Technology in 1997. From 1997 to 2005 he was on the faculty of University of Pennsylvania, Philadelphia PA. With over ninety publications to his name, including a cover story in the May 2005 issue of Scientific American, his scholarship has been recognized by several distinguished honors, including the National Institute of Health Director’s Pioneer Award in 2006. In 2016, he was named a fellow of the Institute of Electrical and Electronic Engineers and of the American Institute for Medical and Biological Engineering. His 2007 TED talk, A Computer that Works like the Brain, has been viewed over half-a-million times.

Event Tags:


IEEE SCV Computational Intelligence Society
IEEE SCV – Solid State Circuits Society (SSCS) Chapter
IEEE SCV – Circuits and Systems (CASS) Chapter
IEEE SCV – Engineering in Medicine and Biology (EMBS) Chapter
IEEE SF Bay Area Nanotechnology Council Chapter



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