Two PhD Positions on Neuromorphic Computing with Oscillatory Neural Networks (# of pos: 2)

The Electronic Systems (ES) group within the Department of Electrical Engineering of Eindhoven University of Technology (TU/e) is seeking to hire two outstanding PhD candidates within the Horizon Europe project PHASTRAC.

Project

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. The PHASTRAC consortium includes some of Europe's strongest research groups and industries, covering from device fabrication, circuit and architecture design to end use applications. We will demonstrate a first of its kind analog-to-information computing paradigm with industrial applications such as intelligent vehicle interior design and human-robotics interactions that opens the road for EU leadership in energy efficient edge computing.

Candidates

We are seeking highly skilled and motivated candidates to tackle any of the following research areas:

PhD1: Novel devices for analog neuromorphic computing with oscillatory neural networks. Oscillatory neural networks (ONN) will be implemented with innovative material devices such as phase change insulator-metal transition devices based on VO2 and novel bilayer MO/HfO2 based analog memristors (where MO stands for metal oxide), also referred as MO/HfO2 resistive random-access memories (RRAM). Neurons will be emulated by their oscillatory behaviour via phase change VO2 devices and synapses are emulated via coupling weights among neurons via MO/HfO2 RRAM. This thesis aims to develop physical models of devices to investigate their benefits and costs for implementing analog ONN computing. This thesis will be conducted in collaboration with PHASTRAC project partners.

PhD2: Analog ONN design for multi-sensory cross domain learning and inference. The goal here is to develop in functional hardware the analog ONN computing paradigm for multi-modal sensory data processing for use cases in vehicle intelligent interior systems or human-robot collaboration. Two main paths will be explored with i) conventional CMOS technology and ii) innovative materials and devices throughout the project. FPGA implementation will be also explored as a fast and low-cost implementation of ONN computing paradigm with multi-sensory system and real-time data processing. A tapeout of CMOS ASIC development is envisioned for analog ONN implementation computing for multi-modal online learning and sensory data processing. This thesis will be conducted in collaboration with PHASTRAC project partners.