Two PhD Positions on Neuromorphic Computing with Oscillatory Neural Networks

Irène Curie Fellowship

No

Department(s)

Electrical Engineering

Reference number

V36.5908

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 and Architectures for Neuromorphic Computing based on Oscillatory Neural Networks
Oscillatory neural networks are a brain-inspired computing paradigm that allow for energy efficient and adaptive intelligent systems. By mimicking the human brain and nervous system, these computing architectures are excellent candidates for solving complex and large-scale associative learning problems. The objective of this thesis is the development of device models for emulating neurons and synapses. Oscillator neurons are based on phase change insulator-metal transition devices such as vanadium dioxide VO2 . Coupling synapses are based on memristor devices based on novel bilayer MO/HfO2 (where MO stands for metal oxide), also referred to as MO/HfO2 resistive random-access memories (RRAM). Experimental development of these devices will be performed by the IBM Research Zurich team, a partner in the PHASTRAC project. Based on experimental data measurements, physical device models for VO2 oscillator and MO/HfO2 coupling will be developed. The interplay between the oscillators and coupling will be investigated to analyze the impact of device nonuniformities and their impact on ONN performance, reliability and energy. ONN circuit simulations will be performed to analyze stochastic noise and harmonic injection on the oscillators. ONN architecture design and design space exploration will be performed and evaluated for various applications such as associative memory, image segmentation, and solving combinatorial problems via the Ising model formulation. This thesis will be conducted in collaboration with PHASTRAC project partners.

PhD2: Neuromorphic computing with oscillatory neural networks for multi-sensory cross-domain learning and inference.
In this PhD work, the candidate will investigate neuro-inspired computing architecture where information is encoded in the phase of coupled oscillating neurons or oscillatory neural networks (ONN). The goal is to develop functional hardware for the ONN computing paradigm, such as i) digital and ii) analog design, to allow a seamless interface between sensors and processing to take decisions and reason based on sensed data. The goal is to process analog sensor signals from different sources, such as audio, image, strain, temperature, etc, to enable multi-modal sensors data processing for use cases in the autonomous car (e.g., vehicle intelligent interior) or human-robot collaboration. To design ONN computing architecture, two main paths will be explored. First, digital implementation of ONNs in FPGA for rapid prototyping and implementation on edge applications. Second, analog and/or mixed-signal circuit design with conventional CMOS technology based on open-source PDK to implement a first-ever CMOS ASIC ONN chip as a proof-of-concept for multi-modal sensor data processing. An important aspect of this work will be focused on the development of algorithms for multi-modal online learning and sensor data processing. This thesis will be conducted in collaboration with PHASTRAC project partners.

For both positions we are looking for excellent, teamwork-oriented, and research-driven candidates with an Electrical Engineering or related background and strong hardware/software design skills. Applications from computer science and AI MSc students with affinity for hardware implementation are also welcomed.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale 27 (min. €2,541 max. €3,247).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own trainings program .
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

Electronic Systems group

The Electronic Systems group (tue.nl/es) is a top research group consisting of five full professors, two associate professors, seven assistant professors, several postdocs, about 40 PDEng and PhD candidates, and support staff. The ES group is world-renowned for its design automation and embedded systems research. It is our ambition to provide a scientific basis for design trajectories of electronic systems, ranging from digital circuits to cyber-physical systems. The trajectories are constructive and lead to high-quality, cost-effective systems with predictable properties (functionality, timing, reliability, power dissipation, and cost). Design trajectories for applications that have strict real-time requirements and stringent power constraints are an explicit focus point of the group.

Information

Do you recognize yourself in this profile and would you like to know more?
Please contact dr.ir. Sander Stuijk, email s.stuijk[at]tue.nl, http://www.es.ele.tue.nl/~sander.

For information about terms of employment, click here or contact Mrs. Linda van den Boomen, HR advisor, email  l.j.c.v.d.boomen[at]tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact [Name Surname], [Job title], …[at]tue.nl or +31 40 247 ….

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Please visit www.tue.nl/jobs and www.tue.nl/en/education/graduate-school/ to find out more about working at TU/e!

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.
• Brief description of your MSc thesis.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.