https://spectrum.ieee.org/memristor-devices-ai
"Memristive devices that mimic neuron-connecting synapses could serve as the
hardware for neural networks that copy the way the brain learns. Now two new
studies may help solve key problems these components face not just with yields
and reliability, but with finding applications beyond neural nets.
Memristors, or memory resistors, are essentially switches that can remember
which electric state they were toggled to after their power is turned off.
Scientists worldwide aim to use memristors and similar components to build
electronics that, like neurons, can both compute and store data. These
memristive devices may greatly reduce the energy and time lost in conventional
microchips shuttling data back and forth between processors and memory. Such
brain-inspired neuromorphic hardware may also prove ideal for implementing
neural networks—AI systems increasingly finding use in applications such as
analyzing medical scans and empowering autonomous vehicles.
However, current memristive devices typically rely on emerging technologies
with low production yields and unreliable electronic performance. To help
overcome these challenges, researchers in Israel and China fabricated
memristive devices using a standard CMOS production line. The resulting silicon
synapses the team built boasted a 100 percent yield with 170- to 350-fold
greater energy efficiency than a high-performance Nvidia Tesla V100 graphics
processing unit when it came to multiply-accumulate operations, the most basic
operation in neural networks."
Via Wayne Radinsky.
Cheers,
*** Xanni ***
--
mailto:xanni@xanadu.net Andrew Pam
http://xanadu.com.au/ Chief Scientist, Xanadu
https://glasswings.com.au/ Partner, Glass Wings
https://sericyb.com.au/ Manager, Serious Cybernetics
