Postdoc Position on Neuromorphic Computing with Oscillatory Neural Networks
Postdoc Position on Neuromorphic Computing with Oscillatory Neural Networks
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Job description
The Electronic Systems (ES) group within the Department of Electrical Engineering of Eindhoven University of Technology (TU/e) is seeking to hire an outstanding Postdoc candidate 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.
Candidate
We are seeking highly skilled and motivated candidates to work on Neuromorphic ONN Computing and Multi-modal ONN learning.
The Postdoc will explore analog ONN neuromorphic computing paradigm with novel devices based on VO2 (oscillators) and MO/HfO2 (synaptic interconnections) materials. The focus is to develop circuit to architecture-level modules to design the mechanism for updating the non-volatile interconnection network depending on the transient state of the network. The overall objective is to implement learning functions in analog weights and realizable by MO/HfO2 devices. Various coupling schemes will be implemented and investigated with respect to the various learning rules and problems to be solved. Different problems will require different coupling schemes and learning rules; thus, the goal is to thoroughly assess the analog ONN requirements, learning mechanisms and its design implementation. We will also investigate the interface with sensor data to investigate reliability, performance, and noise sensitivity.
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.
Candidate
We are seeking highly skilled and motivated candidates to work on Neuromorphic ONN Computing and Multi-modal ONN learning.
The Postdoc will explore analog ONN neuromorphic computing paradigm with novel devices based on VO2 (oscillators) and MO/HfO2 (synaptic interconnections) materials. The focus is to develop circuit to architecture-level modules to design the mechanism for updating the non-volatile interconnection network depending on the transient state of the network. The overall objective is to implement learning functions in analog weights and realizable by MO/HfO2 devices. Various coupling schemes will be implemented and investigated with respect to the various learning rules and problems to be solved. Different problems will require different coupling schemes and learning rules; thus, the goal is to thoroughly assess the analog ONN requirements, learning mechanisms and its design implementation. We will also investigate the interface with sensor data to investigate reliability, performance, and noise sensitivity.
Specifications
- max. 38 hours per week
- Eindhoven View on Google Maps
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Eindhoven University of Technology (TU/e)
Requirements
For this position we are looking for excellent, teamwork-oriented, and research-driven candidates with a PhD degree in Electrical Engineering, Computer Science or AI related topic and strong hardware/software design skills.
Conditions of employment
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:
A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.
- Full-time employment for one year.
- Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10.
- A year-end bonus of 8.3% and annual vacation pay of 8%.
- High-quality training programs on general skills, didactics and topics related to research and valorization.
- An excellent technical infrastructure, on-campus children's day care and sports facilities.
- Partially paid parental leave and an allowance for commuting, working from home and internet costs.
- A personal development program aimed to develop your social and communication skills (see http://www.tue.nl/PROOF3TU).
- Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.
- A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.
