Artificial Synapses for AI
IEEE Spectrum describes recent progress in the development of nanoscale memory cells that may be applied to variable artificial synapses for artificial neural networks, reported here.
This describes work at IBM Research on an electrochemical random-access memory cell, or ECRAM, where a gate drives lithium ions into or out of a tungsten trioxide channel, changing the channel resistance. What is required for neural network applications is a precise change in resistance, depending on the drive voltage, which is rapid and repeatedly reversible. This was presented at the International Electron Device Meeting in San Francisco in December. Other related work reported at IEDM included novel ferroelectric FETs (FeFET) from Purdue University, University of Notre Dame, and Samsung, which may also be applied to chips for neural networks.