Breakthroughs in Neuromorphic Computing Could Speed Computers and AI By Ten Times

Researchers at Sandia National Laboratories have collaborated with Stanford University and University of Massachusetts, Amherst have made recent breakthroughs in neuromorphic computing and the broader fields of organic electronics and solid-state electrochemistry.












Science – Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and read out of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and less than 10 nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory (IFG) array based upon a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a transistor array is executed in parallel by overcoming the bridging voltage threshold of the CBMs. Synaptic weight read-out with currents less than 10 nanoampere is achieved by diluting the conductive polymer in an insulating channel to decrease the conductance. The redox transistors endure over billion ‘read-write’ operations and support over 1 megahertz ‘read-write’ frequencies.

SOURCES- Sandia Labs, Journal Science
Written By Brian Wang,