MSc thesis projects - Cognitive sensor nodes and systems
[2023] A factor-graph representation for bio-plausible local learning rules
In this project, we will investigate a factor-graph representation to derive local learning rules that will allow for low-cost adaptive AI hardware.
[2023] Meta-learning for low-cost adaptive neuromorphic hardware
In this project, we will investigate meta-learning, an emerging machine learning technique that allows tuning the training process in order to learn faster and more efficiently (i.e. learning-to-learn), to compensate for the non-idealities of synaptic plasticity in neuromorphic hardware.
[2023] Mixed-signal neuromorphic hardware for sub-10µW on-chip online learning at the edge
In this project, we will investigate how a few well-chosen analog blocks can boost the efficiency of a neuromorphic architecture for on-chip online learning at the edge.
[2023] A hardware architecture based on the least-control principle for bio-plausible local online learning
In this project, we will investigate a neuromorphic hardware implementation of the least-control principle (LCP), which offers a bio-plausible way of training artificial neural networks.
[2023] Monostable multivibrator networks for low-cost spike-based computing
In this project, we will explore monostable multivibrator (MMV) networks for low-cost spike-based computing and deploy them in a real-world event-based processing use case.
[2023] Dendritic circuits in neuromorphic hardware
In this project, we will investigate a key element of biological neurons: dendrites. This will include a survey of the key dendritic functions, of which a subset will then be implemented in custom neuromorphic hardware.
[2023] Sparse spiking neural networks for radar-based human activity recognition
In this project, we will minimize the computational footprint of always-on human activity recognition by taking inspiration from the brain, where neurons transiently exhibit high spike-based activity patterns only when unexpected input is received.
