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Headline Stories Sure to Rattle Memristor-Denier Tim H.

Ferroelectric Memristors and Exotic Materials to Drive AI

Memristor Breakthrough Transforms Analog Computing

Memristor Breakthrough Transforms Analog Computing - RF CafeRuh-roh, here is another science news item sure to memristor denier and über nimrod Tim H., who for years sent me harassing e-mails to inform me of how memristors are only theoretical entities. He got downright abusive and threatening. Maybe someday I'll reveal his identity ... but I digress. This article from the SciTechDaily website reports on work done at the University of Southern California. It begins: "Researchers have made significant advances in memristor technology, enhancing its precision and efficiency. This innovation promises to bridge the gap between analog and digital computing, offering faster, more energy-efficient processing suitable for AI, machine learning, and beyond..."

1st Complete Memristor-Based Bayesian Neural Network Implementation

1st Complete Memristor-Based Bayesian Neural Network Implementation - RF CafeConsidering medical-diagnosis and other safety-critical, sensory-processing applications that require accurate decisions based on a small amount of noisy input data, the study notes that while Bayesian neural networks excel at such tasks because they provide predictive uncertainty assessment, their probabilistic nature requires increased use of energy and computation. The increase is caused by the fact that implementing the networks in hardware requires a random number generator to store the probability distributions, i.e. synaptic weights. "Our paper presents, for the first time, a complete hardware implementation of a Bayesian neural network utilizing the intrinsic variability of memristors to store these probability distributions," said Elisa Vianello, CEA-Leti chief scientist. "We exploited the intrinsic variability of memristors to store these probability distributions, instead of using random number generators." A team comprising CEA-Leti, CEA-List and two CNRS laboratories...

How Memristors Help Machines Think at Different Timescales

"In the latest episode of Brains and Machines, EE Times regular Dr. Sunny Bains talks to Professor Melika Payvand, who designs neural systems from the circuit-level up at the Institute of Neuroinformatics in Zurich. You’ll find out the role that memristors are playing in the systems she designs, why neural circuits need to operate at different timescales, and why copying some features of biological dendrites could add computational power to silicon brains. Discussion follows with Dr. Giulia D'Angelo from the Italian Institute of Technology and Professor Ralph Etienne-Cummings from Johns Hopkins University. Welcome to Brains and Machines, a deep dive into neuromorphic engineering and biologically inspired technology. In this episode, EE Times regular Sunny Bains talks to Professor Melika Payvand, who designs neural systems from the circuit-level up at the Institute of Neuroinformatics in Zurich. You'll find out the role that memristors are playing in the systems she designs..."

Memristor Semiconductor Building Block

Memristor Semiconductor Building Block - RF Cafe Uh-oh, I wonder if the dummkopf who keeps writing to call me an idiot for claiming memristors are real will crawl out of his hole again? "The world's first fully system-integrated memristor chip has been unveiled by a team of Chinese scientists who believe it could not only make artificial intelligence smarter, but also more time and energy efficient. While the semiconductor has yet to leave the lab setting, it could allow for the development of AI that is capable of more human-like learning, which could have implications for the way smart devices and autonomous driving work, according to the researchers. 'Learning is highly important,' for edge intelligence devices, the research team from Tsinghua University said in their study released in the journal Science on September 15, referencing devices that process data internally with technology like AI..."

HPE Invents 1st Memristor Laser*

HPE Invents First Memristor Laser - RF Cafe"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."

Memristors Run AI Tasks at 1/800th Power

Memristors Run AI Tasks at 1/800th Power - RF CafeHere'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..."

Memristor schematic symbol - RF CafeThe 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.

Resistor, capacitor, inductor, and memristor symmetry (Wikipedia) - RF CafeTo 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.

Bayesian Machine Based on Memristors

A Bayesian Machine Based on Memristors - RF Cafe"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..."

Memristor Crossbar-Based Learning System for AI

Memristor Crossbar-Based Learning System for AI - RF Cafe "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..."

Memristor-Based Reservoir Computing System

Memristor-Based Reservoir Computing System - RF Cafe"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..."



Last updated January 13, 2024

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