Project description
Novel oscillatory neural network technology for improved human-machine interactions and nanoelectronics
Our world has never been more digitised. As AI applications and nanoelectronics continue to grow, the use of sensors is important. Currently, most sensors receive analogue inputs from the real world and generate analogue signals to be processed. The inevitable digitisation of these signals, however, will create enormous amounts of raw data requiring a lot of memory and high power consumption. As the number of sensor-based IoTs grows, bandwidth limitations will also need to be addressed. In this context, the EU-funded PHASTRAC project will develop an analogue-to-information neuromorphic computing paradigm based on oscillatory neural networks (ONNs). The aim of this ONN computing architecture will be to seamlessly interface with sensors and process their analogue data without any analogue-to-digital conversion.
Objective
In recent years, we have witnessed an explosion of artificial intelligence (AI) applications which will continue to grow over the next decade. An intelligent and digitized society will be ubiquitous, enabled by increased advances in nanoelectronics. Key drivers will be sensors interfacing with the physical world and taking appropriate action in a timely manner while operating with energy efficiency and flexibility to adapt. The vast majority of sensors receive analog inputs from the real world and generate analog signals to be processed. However, digitizing these signals not only creates enormous amount of raw data but also require a lot of memory and high-power consumption. As the number of sensor-based IoTs grows, bandwidth limitations make it difficult to send everything back to a cloud rapidly enough for real-time processing and decision-making, especially for delay-sensitive applications such as driverless vehicles, robotics, or industrial manufacturing.
In this context, PHASTRAC proposes to develop a novel analog-to-information neuromorphic computing paradigm based on oscillatory neural networks (ONNs). We propose a first-of-its-kind and novel analog ONN computing architecture to seamlessly interface with sensors and process their analog data without any analog-to-digital conversion. ONNs are biologically inspired neuromorphic computing architecture, where neuron oscillatory behavior will be developed by innovative phase change VO2 material coupled with synapses to be developed by bilayer Mo/HfO2 RRAM devices. PHASTRAC will address key issues 1) novel devices for implementing ONN architecture, 2) novel ONN architecture to allow analog sensor data processing, and 3) processing the data efficiently to take appropriate action. This “sensing-to-action” computing approach based on ONN technology will allow energy efficiency improvement 100x-1000x and establish a novel analog computing paradigm for improved future human-machine interactions.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesinternetinternet of things
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
- engineering and technologynanotechnologynanoelectronics
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Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
5612 AE Eindhoven
Netherlands