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 promptly while operating with energy efficiency and flexibility to adapt. Most sensors receive analog inputs from the real world and generate analog signals to be processed. However, digitizing these signals creates not only an enormous amount of raw data but also requires a lot of memory and high-energy consumption. As the number of sensor-based IoTs grows, bandwidth limitations and privacy aspects make it impossible to send everything back to a cloud for real-time processing and decision-making, especially for delay or privacy-sensitive applications such as driverless vehicles, robotics, personal healthcare, or industrial manufacturing.
In this context, PHASTRAC aims to develop a novel analog-to-information neuromorphic computing paradigm based on oscillatory neural networks (ONNs). We offer 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 a biologically inspired neuromorphic computing architecture, where neuron oscillatory behavior will be developed by innovative phase change VO2 material coupled with synapses developed by bilayer Mo/HfO2 RRAM devices. PHASTRAC will address the most critical issues, namely 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 improvements of 100x-1000x and establish a novel analog computing paradigm for improved human-machine interactions.
The main objectives of PHASTRAC are:
(1) fabrication of low-power devices based on phase-change materials and bilayer metal-oxide RRAM devices that can serve as the building blocks for oscillatory neural networks (ONNs)
(2) development of devices, circuits to architecture-level models to allow exploration of the oscillatory neural network computing paradigm to allow the development of dedicated algorithms for ONN training
(3) development of a sensing-to-action computing paradigm with ONN that can serve as an energy efficient computing platform with potential interests in edge computing
(4) development of demonstrators and applications to show the advantage of ONN computing for AI tasks
