Lecturers
Rodolphe Sepulchre, KU Leuven and University of Cambridge
Tom Chaffey, University of Cambridge
Schedule and place
This 15-hour course will take place in six sessions over three days at KU Leuven
Dates:
29 November - 30 November : Aula Arenberg Castle, Kasteelpark Arenberg 1, 3001 Heverlee
1 December : Aula Thermotechnisch instituut, Kasteelpark Arenberg 41, 3001 Heverlee
Schedule:
10:00 - 12:30 (morning session)
14:00 - 16:30 (afternoon session)
Parking De Molen:
For the parking there is a daily code : to be used for entering AND leaving the parking
- 29/11: 8570#
- 30/11: 9285#
- 01/12: 8416#
Travel instructions: https://www.esat.kuleuven.be/english/info/route
Abstract
Event-based technology is developing at a fast pace (e.g. via neuromorphic computing and event-based cameras) but we currently lack a control theory of event-based systems. The general aim is to conceive physical machines that combine the reliability of discrete automata and the robustness and adaptation of physical control systems.
The theory of mixed feedback systems aims at leveraging the existing theory of control, optimization, and learning while acknowledging the inherent out-of-equilibrium nature of event-based machines.
The theory is grounded in the operator-theoretic framework of maximal monotonicity in Reproducing Kernel Hilbert Spaces. The course will introduce those mathematical tools at a basic level and present how they can be used to analyze and design neuromorphic event-based machines.
Description
The course consists of six sessions over three days. Each session will cover one of the following modules
Module1: Feedback and memory (RS)
1.1. Examples of mixed feedback systems. Motivation. Current limitations of control theory, machine learning, and neuromorphic engineering.
1.2. Static mixed feedback
1.3. Dynamic mixed feedback. Questions and challenges
Module 2: Feedback system analysis (TC)
2.1. Systems as operators. Phase and Gain. Monotonicity and Lipschitz operators. The passivity and the small-gain theorems.
2.2. Graphical representations. Nyquist plots. Scaled Relative Graphs.
2.3. Mixed feedback systems. Mixed gain and mixed phase feedback systems.
Module 3: Feedback algorithms (TC)
3.1. Fixed point algorithms and zero finding algorithms.
3.2. Splitting algorithms and circuit representations
3.3. Algorithmic solutions of mixed feedback systems
Module 4: Modelling at scale (RS)
4.1. Reproducing Kernel Hilbert Spaces
4.2. Gradient systems in Reproducing Kernel Hilbert Spaces
4.3. Feedback neural networks at scale
Module 5: Tuning at scale (RS)
5.1. Recursive least square estimation
5.2. A robust internal model principle for mixed feedback systems
5.3 Adaptive control and online learning at scale
Module 6: Neuromorphic control (RS)
6.1. Physical Spiking Neural Networks
6.2. Control as neuromodulation at scale
6.3. Neuromorphic event-based machines
Course material
- Slides, exercises, and references will be available
Evaluation
- An assignment that will be released during the course