AEDAT

A Python package to decode AEDAT 4 files from event cameras with a Rust implementation for speed.

Overview

The project aedat on GitHub is a fast AEDAT 4 python decoder with a Rust implementation, allowing users to efficiently read .aedat data files. It facilitates the processing of event-based data, commonly used in neuromorphic computing and vision systems. Users can easily install the library using pip and apply it to read and process frames using popular Python libraries like Pillow and OpenCV. The repository includes examples and detailed instructions on creating decoder objects, iterating through data packets, and handling different types of events or frames. Licensed under MIT, it is an open-source tool designed for flexibility and speed in handling AEDAT files.

Can you contribute tutorial guides or case studies?

Get Involved with ONM

Efficient Compression for Event-Based Data in Neuromorphic Applications

Efficient Compression for Event-Based Data in Neuromorphic Applications

  • Gregor Lenz, Fabrizio Ottati, Alexandre Marcireau

Discover methods to efficiently encode and store event-based data from high-resolution event cameras, striking a balance between file size and fast retrieval for spiking neural network training.

TrueNorth: A Deep Dive into IBM's Neuromorphic Chip Design

TrueNorth: A Deep Dive into IBM's Neuromorphic Chip Design

  • Fabrizio Ottati

Explore the innovative TrueNorth neuromorphic chip, its event-driven architecture, low power operation, massive parallelism, real-time capabilities, and scalable design.

Spiking Neural Network (SNN) Library Benchmarks

Spiking Neural Network (SNN) Library Benchmarks

  • Gregor Lenz, Kade Heckel, Sumit Bam Shrestha, Cameron Barker, Jens Egholm Pedersen

Discover the fastest Spiking Neural Network (SNN) frameworks for deep learning-based optimization. Performance, flexibility, and more analyzed in-depth