Combining Spiking Neural Networks with Hyperdimensional Computing for Autonomous Navigation
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 , whereas HDC have proven to be capable to update their simple to cope with environmental changes during operation .
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 , and later extend it with an HDC regressor.
- Machine Learning
- 20% Theory
- 80% Implementation
-  Gehrig et al., Event-Based Angular Velocity Regression with Spiking Networks
-  Hersche et al., Integrating Event-based Dynamic Vision Sensors with Sparse Hyperdimensional Computing: A Low-power Accelerator with Online Learning Capability
-  MVSEC dataset