Diffusive Memristor as a Synapse

Neuromorphic computing, systems designed to mimic the biological nervous system, require far less power than current computer processors. The increased efficiency makes feasible artificial intelligence applications for smaller, hand-held devices (e.g. smartphones, tablets).  To this end, UMass inventors have designed hardware components that mimic neuronal synapses (Figure A). Specifically, diffusive Ag-in-oxide memristors show a temporal response during and after stimulation similar to that of a biological synapse. The novel diffusive memristor and its synapse-like dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses and represent a major advancement in a hardware implementation for neuromorphic computing.  

TECHNOLOGY DESCRIPTION

ADVANTAGES

•       High density

•       Low energy

•       faithfully emulation of bio-synapses

•       Intrinsic synaptic dynamics

•       3D stackable

APPLICATIONS

•       Memristor for neuromorphic computing

•       Spiking neural networks (SNNs) 

•       Synapse emulators 

AVAILABILITY:

Available for Licensing and/or Sponsored Research

DOCKET:

UMA 18-001

PATENT STATUS:

Patent Issued

NON-CONFIDENTIAL INVENTION DISCLOSURE

LEAD INVENTOR:

Qiangfei Xia, Ph.D.  Joshua Yang, Ph.D.

CONTACT:

Neuromorphic computing, systems designed to mimic the biological nervous system, require far less power than current computer processors. The increased efficiency makes feasible artificial intelligence applications for smaller, hand-held devices (e.g. smartphones, tablets).  To this end, UMass inventors have designed hardware components that mimic neuronal synapses (Figure A). Specifically, diffusive Ag-in-oxide memristors show a temporal response during and after stimulation similar to that of a biological synapse. The novel diffusive memristor and its synapse-like dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses and represent a major advancement in a hardware implementation for neuromorphic computing.