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Neuromorphic implementation of a spiking neural network (SNN) for optimizing water supply
Water management is critical to agriculture, especially with increasing climate change and water scarcity concerns. Traditional water supply systems often rely on fixed schedules or basic sensor feedback to control irrigation or water distribution, which can result in inefficiencies and wastage. With the rapid advancements in machine learning and neuromorphic computing, there is an opportunity to develop smarter, more adaptive water supply systems. Spiking Neural Networks (SNNs) on a hardware substrate[1], inspired by the brain's efficient way of processing information, offer a promising solution due to their event-based processing and low energy consumption.
This project proposes the development of a neuromorphic implementation of a Spiking Neural Network on DYNP-SE1[2] to optimize water supply using real-time moisture datasets [3]. The SNN will learn and adapt to environmental changes, ensuring that water is supplied only when necessary, reducing waste, and optimizing water usage.
Taking advantage of the current technologies available in the NCS group, we will implement the feedback-control algorithm on the DYNAP-SE chip [1].
1. Getting hands-on experience with the DYNAP-SE chip, and a winner-take-all attractor network.
2. Start-end of water supply time detected with a threshold crossing methods.
3. Taking into consideration watering schedule constraints and further optimization of the algorithm implementing an on-the-edge moving threshold
4. Implementing local fault detection mechanisms. (optional)
This project can be tailored on both a semester project and a master thesis project.
Taking advantage of the current technologies available in the NCS group, we will implement the feedback-control algorithm on the DYNAP-SE chip [1].
1. Getting hands-on experience with the DYNAP-SE chip, and a winner-take-all attractor network.
2. Start-end of water supply time detected with a threshold crossing methods.
3. Taking into consideration watering schedule constraints and further optimization of the algorithm implementing an on-the-edge moving threshold
4. Implementing local fault detection mechanisms. (optional)
This project can be tailored on both a semester project and a master thesis project.
A neuromorphic system based on SNNs can address optimise water supply based on moisture data by leveraging real-time sensor data in an energy-efficient manner.
A neuromorphic system based on SNNs can address optimise water supply based on moisture data by leveraging real-time sensor data in an energy-efficient manner.
