Neuromorphic computing

Data manipulation (memory, computation, communications, data mining) in its many forms drives and fuels our civilization. Revolutionary developments in the past decades in hardware (principally CMOS technology) and software (such as machine-learning), has fueled the ever-increasing capabilities of modern computational machines. It is however agreed that these enhanced computational capabilities will soon slow down considerably due to a variety of limitation, principally because of the large energy consumption expected. On the other hand, nature has evolved a computational machine (the “brain”) which has substantial advantages in energy efficiency over conventional silicon-based computers.

I will describe the recent worldwide attempts [1] which bring us closer to the holy grail of this issue:

“Create a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain”.

This work was supported as part of the Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0019273

[1] The work of the Q-MEEN-C group described at https://qmeenc.ucsd.edu