A neuromorphic-computing ‘roadmap

Electrical engineers at the Georgia Institute of Technology have  published a roadmap that details innovative analog-based techniques that they believe could make it possible to build a practical neuromorphic (brain-inspired) computer while minimizing energy requirements. The roadmap was published in the journal Frontiers in Neuroscience (open access).
“A configurable analog-digital system can be expected to have a power efficiency improvement of up to 10,000 times compared to an all-digital system,” said Jennifer Hasler, a professor in the Georgia Tech School of Electrical and Computer Engineering (ECE) and a pioneer in using analog techniques for neuromorphic computing.
“To my knowledge, this is the first time a detailed neuromorphic roadmap has been attempted,” said Hasler. “We describe specific computational techniques could offer real progress in neuromorphic systems.”
Unlike digital computing, in which computers can address many different applications by processing different software programs, analog circuits have traditionally been hard-wired to address a single application.
For example, cell phones use energy-efficient analog circuits for a number of specific functions, including capturing the user’s voice, amplifying incoming voice signals, and controlling battery power.