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Combining Spiking Neural Networks with Hyperdimensional Computing for Autonomous Navigation

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In the last 10 years, Artificial Neural Networks (ANN) revolutionized many scientific fields by solving very difficult practical problems. Convolutional Neural Networks (CNN) is a very popular approach which allowed to reach state-of-the-art accuracies in many different machine learning tasks, featuring a reasonably low memory footprint. In the last years, a new categories of efficient sensors is meeting a growing interest; ULP event-based cameras and audio sensors belong to this category. To efficiently exploit the nature of the data produced by such sensors, a paradigm shift in the way data are acquired and processed could be unavoidable. For this reason, research communities started focusing their interests on less conventional computing paradigms, such as brain-inspired, event-driven computing. Here, both Spiking Neural Networks (SNN) as well as Hyperdimensional Computing (HDC) showed promising results in processing event-based data. Specifically, SNNs have shown to extract robust features from event-driven cameras [1], whereas HDC have proven to be capable to update their simple to cope with environmental changes during operation [2].

In this project, you combine the best of the two worlds (SNNs for feature extraction and HDC for regression) for steering angle prediction based on event-driven cameras. You start by applying an already given SNN on the publicly available MVSEC dataset [3], and later extend it with an HDC regressor.

Status: Available

Looking for 1-2 students for a semester project.
Supervision: Alfio Di Mauro


  • Machine Learning
  • Python


20% Theory
80% Implementation


  • [1] Gehrig et al., Event-Based Angular Velocity Regression with Spiking Networks
  • [2] Hersche et al., Integrating Event-based Dynamic Vision Sensors with Sparse Hyperdimensional Computing: A Low-power Accelerator with Online Learning Capability
  • [3] MVSEC dataset


Luca Benini

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Practical Details

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