In 1971, Leon O. Chua published a seminal paper on the missing basic circuit element. Leon O. Chua and Sung-Mo Kang published a paper, in 1976, that described a large class of devices and systems they called memristive devices and systems. Just recently, Stan Williams and his research team at HP Labs unveiled a two-terminal titanium dioxide nanoscale device in Nature magazine that exhibited memristor characteristics. This symposium will explore the potential of memristors and memristive systems as they advance state of the art nano-electronic circuits. Program (Part 2) Memristors as Synapses in a Neural Computing Architecture Greg Snider, Senior Architect, Information and Quantum Systems Laboratory, Hewlett-Packard Laboratories Prospects and Challenges of Redox-based Memristive RRAM Concpets Rainer Waser, RWTH Aachen University at Research Center Juelich, Germany The event is co-sponsored by UC Merced and UC Berkeley in cooperation with the Semiconductor Industry Association (SIA). The Symposium is funded by the National Science Foundation.

Recently, computation in memory becomes very hot due to the urgent needs of high computing efficiency in artificial intelligence applications. In contrast to von-neumann architecture, computation in memory technology avoids the data movement between CPU/GPU and memory which could greatly reduce the power consumption.Memristor is one ideal device which could not only store information with multi-bits, but also conduct computing using ohm’s law. To make the best use of the memristor in neuromorphic systems, a memristor-friendly architecture and the software-hardware collaborative design methods are essential, and the key problem is how to utilize the memristor’s analog behavior.We have designed a generic memristor crossbar based architecture for convolutional neural networks and perceptrons, which take full consideration of the analog characteristics of memristors. Furthermore, we have proposed an online learning algorithm for memristor based neuromorphic systems which overcomes the varation of memristor cells and endue the system the ability of reinforcement learning based on memristor’s analog behavior. Full abstract and speaker details can be found here: https://nus.edu/3cSFD3e Register for free for all our upcoming webinars at https://nus.edu/3bcN9pS.

HKU EEE x IEEE HK ED/SSC Distinguished Speaker Series Speaker: Prof. Yuchao Yang, Director of Center for Brain Inspired Chips, Peking University Date: 27 August 2021 For more seminars, see https://r10.ieee.org/hk-edssc/

Memristive devices have been a hot topic in nanoelectronics for the last two decades in both academia and industry. Originally proposed as digital (binary) nonv

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Nature Electronics - This Review examines switching mechanisms in memristive devices based on van der Waals materials, and explores the advantages such devices offer and the challenges that must be...

The confluence of Big Data, IoT and Real-Time Analytics calls for rethinking of the hardware computing paradigm, either by the bottom up or top down approach. Memristive neuromorphic systems inspired by brain functions and implemented through new materials properties, bionic memristive devices (e.g., artificial synapses and neurons) and neural network circuits, are emerging with the promise of transforming the semiconductor information processing technology.Since the experimental discovery of memristors twelve years ago, memristive neuromorphic hardware has continued to make big strides. On the level of the material, a huge number of materials of various categories have shown memristive properties and this number continues to increase rapidly. In such a growing filed, there is a crucial need to establish materials selection rules and evaluate the suitability of the corresponding devices for neuromorphic systems. Device yield testing, performance distribution analysis and circuit reliability simulations have to be performed and standardized to turn these materials research endeavors into real impact. On the device level, researchers have showcased devices with diverse physical mechanisms, primary neuromorphic functions, and intriguing performances. Highly reproducible devices with desired characteristics, and versatile devices integrating multiple neural dynamics and computational capabilities remain to be demonstrated to further boost the system performance and functionali...

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Charge density wave (CDW) is a quantum mechanical phenomenon, which induces distortion in the crystal structures of some low-dimensional (1D or 2D) metals, when the temperature is reduced. Such distorted crystal structure is known as CDW phase and its resistivity is much higher than the original symmetric phase. Since the switching between symmetric and CDW phase can also be made by the application of external electric field, these materials are technologically important and have attracted immense attention in the nanoelectronics community.

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IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step

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How manganite-based Memristor Behavior Impacts Faster Learning in Hardware Neural Networks In a groundbreaking study focused on the emerging field of neuromorphic computing, MELON researchers from CONICET-centre, Argentina, have shed new light on the potential of oxide-based memristor arrays with