Work

Event-driven nearshore and shoreline coastline detection on SpiNNaker neuromorphic hardware

Year: 2024

Type: article

Abstract: Coastline detection is vital for coastal management, involving frequent observation and assessment to understand coastal dynamics and inform decisions on environmental protection. Continuous streaming of high-resolution images demands robust data processing and storage solutions to manage large datasets efficiently, posing challenges that require innovative solutions for real-time analysis and meaningful insights extraction. This work leverages low-latency event-based vision sensors coupled with neuromorphic hardware in an attempt to decrease a two-fold challenge, reducing the computational burden to ∼0.375 mW whilst obtaining a coastline detection map in as little as 20 ms. The proposed Spiking Neural Network runs on the SpiNNaker neuromorphic platform using a total of 18 040 neurons reaching 98.33% accuracy. The model has been characterised and evaluated by computing the accuracy of Intersection over Union scores over the ground truth of a real-world coastline dataset across different time windows. The system’s robustness was further assessed by evaluating its ability to avoid coastline detection in non-coastline profiles and funny shapes, achieving a success rate of 97.3%.

Source: Neuromorphic Computing and Engineering

DOI: 10.1088/2634-4386/ad76d5

Authors: Mazdak Fatahi, Pierre Boulet, Giulia D’Angelo

Institutions: Université de Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille, Czech Technical University in Prague

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,700

Funders: İslam Tarih, Sanat ve Kültür Araştırma Merkezi, Centre National de la Recherche Scientifique