Unlimited potential for ultra-fast trainings of neural networks
Digital resistive switches which store data are already widely used as non-volatile random-access memory. Analog resistive switches are used as reconfigurable weights in neural networks. Use the MEMRiSTOR to store and process data in the same memristor device in real-time and without data transfer steps. Exploit the unique feature of the MEMRiSTOR and implement disruptingly new algorithms in top-level applications, e.g. autonomous driving, IoT edge computing, big data analytics, and neuromorphic computing.
Large data sets, Real time video, imaging and voice data interpretation, e.g. from traffic, factories, safety and security areas, shopping centres, medical, governmental, administrative and scientific areas
Finding new correlations in large data sets, e.g., for the purpose of advancing data analysis
Ultra-fast forecasting, e.g. of environmental, economical, or social threats, e.g., storm floods, market turmoils, supply bottlenecks
Edge sensor data processing and decision-making for operating industrial facilities, production lines, power stations, etc. enables faster detection of critical states, faster reaction and less repair- and downtimes.
Pattern recognition from sensor-edge data processing and classification for the development of autonomous machines, robots, and systems esp., in the context of machine-machine and human-machine collaboration
Tracking and teaching of hand and body movements for the development of products (data glasses, gloves) for e.g. instructing unskilled production personnel to correctly execute manufacturing processes
Real-time information from the building management system about the critical status of individual systems, e.g. heating or air-conditioning system
Consolidation of data from a wide variety of individual systems in the building for the purpose of developing new hardware, e.g. wearables or intelligent household helpers, or Real-time monitoring of systems, e.g. heating, air-conditioning, maintenance, reduction of energy consumption and loss
Pattern recognition from sensor based data processing and classification for the development of smart wearables, e.g. smart glasses that can predict situations and thus assist and instruct humans in daily living