As a final test, Kim’s team explored how its device would perform if it were to carry out actual learning tasks such as recognizing samples of handwriting. The team ran a computer simulation of an artificial neural network consisting of three sheets of neural layers connected via two layers of artificial synapses with the properties of the actual neuromorphic chip. They fed into their simulation tens of thousands of samples from a handwritten recognition dataset commonly used by neuromorphic designers, and found that their neural network hardware recognized handwritten samples 95 percent of the time, compared to the 97 percent accuracy of existing software algorithms.
The team is in the process of building a working neuromorphic chip that can carry out handwriting-recognition tasks. Beyond handwriting, the artificial synapse design will enable much smaller, portable neural network devices that can perform complex computations that currently are only possible with large supercomputers.
“Ultimately we want a chip as big as a fingernail to replace one big supercomputer,” said Kim. “This opens a stepping stone to produce real artificial hardware.”