 "Researchers at Hewlett Packard Labs, where
the first practical memristor was created, have invented a new variation on the
device - a
memristor laser..." Also, "From
Transistor to Memristor: Switching Technologies for the Future."
 Here's another news tidbit to tweak the
idiot who keeps harassing me for daring to claim
memristors are real
entities. "These brain-mimicking devices boast tiny energy budgets and hardened
circuits. 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. 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..."
 "Reservoir computing (RC) is an approach
for building computer systems inspired by current knowledge of the human brain.
Neuromorphic computing architectures based on this approach are comprised of dynamic
physical nodes, which combined can process spatiotemporal signals. Researchers at
Tsinghua University in China have recently created a new RC system based on
memristors, electrical components that regulate the flow of electrical current
in a circuit, while also recording the amount of charge that previously flowed through
it..."
 "A chip consisting of
memristor crossbars was trained using a local on-chip learning algorithm. The
team demonstrated that their approach could accurately reconstruct Braille representations
of nine famous computer scientists from highly distorted inputs. Deep-learning models
have proven to be highly valuable tools for making predictions and solving real-world
tasks that involve the analysis of data. Despite their advantages, before they are
deployed in real software and devices such as cell phones, these models require
extensive training in physical data centers, which can be both time and energy consuming.
Researchers at Texas A&M University, Rain Neuromorphics and Sandia National
Laboratories have recently devised a new system for training deep learning models
more efficiently and on a larger scale. This system, introduced in a paper published
in Nature Electronics, relies on the use of new training algorithms and memristor
crossbar hardware, that can carry out multiple operations at once..."
 The
term memristor
- a portmanteau of "memory" and "resistor" - is the fourth fundamental electronic
component, along with the resistor,
capacitor, and
inductor. The name was coined in 1971, which sounds like yesterday to someone
like me (born in 1958), but incredibly that is now half a century ago.
Until fairly recently, the memristor was merely a theoretical curiosity existing
in academic papers. In April of 2008, HP Labs (Hewlett-Packard) reported on successfully
building a nanoscale
memristor in their R&D lab. As with all new technologies, since that time
much progress has been made.
 To the left is a conceptual diagram illustrating
the symmetry of the four basic circuit components - the resistor, the capacitor,
the inductor, and the memristor. Per
Wikipedia: "Chua in his 1971 paper identified a theoretical symmetry between
the non-linear resistor (voltage vs. current), non-linear capacitor (voltage vs.
charge), and non-linear inductor (magnetic flux linkage vs. current). From this
symmetry he inferred the characteristics of a fourth fundamental non-linear circuit
element, linking magnetic flux and charge, which he called the memristor. In contrast
to a linear (or non-linear) resistor, the memristor has a dynamic relationship between
current and voltage, including a memory of past voltages or currents..."
* A nimrod named Tim H. keeps contacting me to say there is no such thing
as a real memristor and calls me bad names while doing so. Maybe someday I'll publish
his messages along with his name, e-mail address and things I've discovered about
him on the Internet.
 "Over the past few
decades, the performance of machine learning models on various real-world tasks
has improved significantly. Training and implementing most of these models,
however, still requires vast amounts of energy and computational power.
Engineers worldwide have thus been trying to develop alternative hardware
solutions that can run artificial intelligence models more efficiently, as this
could promote their widespread use and increase their sustainability. Some of
these solutions are based on
memristors, memory devices that can store information without consuming
energy. Researchers at Université Paris-Saclay- CNRS, Université Grenoble-Alpes-CEA-LETI,
HawAI.tech, Sorbonne Université, and Aix-Marseille Université-CNRS have recently
created a so-called Bayesian machine (i.e., an AI approach that performs
computations based on Bayes' theorem), using memristors. Their proposed system,
introduced in a paper published in Nature Electronics, was found to be
significantly more energy-efficient than currently employed hardware solutions.
"Artificial intelligence is making major progress..."
Posted January 6, 2023
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