Memristor and Memristive Systems Symposium (Part 2) - YouTube
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.
Neuromorphic computing with memristors: from device to system - Professor Huaqiang Wu - YouTube
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.
Memristive Dynamics Based Hardware Primitives for Efficient Computing - YouTube
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/
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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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