Brain Like Chips

 And how might  do we apply them?

Reconfigurable Brain-Like Chips Top Deep Neural Net

By R. Colin Johnson, Commissioned by CACM Staff, April 26, 2022

Today's deep neural networks (DNNs) must be taken off-line to update their learned skills, but real brains update their skill set by reconfiguring their neurons and synapses constantly on the fly—a feat that can now be performed with reconfigurable brain-like chips using quantum materials.

Quantum materials—such as superconductors, topological insulators and even graphene—have remarkable electronic properties that manifest macroscopic quantum mechanical principles that traditional semiconductors only exhibit at the microscopic scale. Now researchers have found a way to form a nickelate (nickel-based) quantum material from the rare earth element neodymium bonded to nickel oxide, NdNiO3 (also known as NNO), with remarkable room-temperature electronic properties that make it ideal for lifelong neural learning hardware, according to a team of researchers from Purdue University, Argonne National Laboratory, the University of Illinois Chicago, Brookhaven National Laboratory, and the University of Georgia.

"The brain is continuously learning," said Purdue University professor Shriram Ramanathan. The formation of new neurons and synapses, a process known as neurogenesis, is ongoing in human brains from birth to death, while today's DNNs are routinely fixed after a lengthy training period, during which they learn a rote function. Ramanathan believes the new quantum material NNO will enable DNNs to learn continuously using neurogenesis, just like human brains.

"Neurogenesis and synaptic rewiring play a crucial role in the formation of memory, and a dynamic brain engaged in life-long learning," said Ramanathan. "Inspired by dynamic reconfiguration in the brain, we hypothesized: if we could mimic neurogenesis behaviors in electrical hardware, we can make AI machines that learn throughout their lifespan."

Funded by the U.S. Department of Energy Office of Science, the U.S. Air Force Office of Scientific Research, and the National Science Foundation (NSF), the researchers formed thin films of NNO on silicon-on-insulator (SOI) -compatible substrates to create proof-of-concept microchips that outperform traditional silicon in deep neural learning hardware. The NNO material demonstrates novel quantum properties that enable it to learn throughout its lifetime, forming new classification categories and deleting old unused ones. It seems ideal for Internet of Things devices on the network's edge, autonomous cars, and many other real-world functions where the world's raw data is in constant flux. .... '