Issue No. 02 - Mar.-Apr. (2017 vol. 19)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MCSE.2017.33
Kwabena Boahen , Stanford University
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, researchers achieved a consequential scale with Neurogrid--the first neuromorphic system that has billions of synaptic connections. Researchers 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.
Transistors, Computers, Neuromorphics, Energy efficiency, Three-dimensional displays, Digital communication, Neural networks, Nanoscale devices, Neuroscience, Robots, Very large scale integration, Moore's Law,scientific computing, neuromorphic, neural engineering, mixed analog-digital systems, system-on-a-chip, network-on-a-chip, asynchronous logic, subthreshold circuits, VLSI, neural networks, computational neuroscience, autonomous robots, embedded computing, cognitive computing
Kwabena Boahen, "A Neuromorph's Prospectus", Computing in Science & Engineering, vol. 19, no. , pp. 14-28, Mar.-Apr. 2017, doi:10.1109/MCSE.2017.33