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Background
Natural Language Processing (NLP), time series analysis and speech recognition are among applications that require specific machine learning techniques for processing temporal data. Reservoir computing (RC) is a machine learning technique that is particularly well-suited for processing temporal data. Typically, the realization of RC systems requires a recurring neural network comprised of layers of volatile and non-volatile devices. Having different devices in different layers adds to the cost, latency, training complexity and footprint of the Reservoir/ Neuromorphic Computing chip.
Description of the invention
Inspired by natural neurons where the transport of Na and K ions forms the basic neural impulses, researchers at the University of Waterloo invented a memristor device that exhibits synapse-like short-term plasticity (STP) behavior. The device is comprised of TiOx, which is known as a good cathode material, and Lithium Phosphorous Oxynitrate (LiPON), a good solid-state Li electrolyte, together forming a device capable of controllable ionic motion under applied voltages, and configurable to either volatile or non-volatile behaviour. The invented universal memristor devices can be used in manufacture of RC/Neuromorphic Computing systems at scale and on the same CMOS layer.
Advantages
Potential applications
The University of Waterloo, renowned for its innovative spirit and co-op education model, excels in cutting-edge research across diverse fields, including advanced manufacturing, artificial intelligence, sustainability, and health technologies.
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