4 papers
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
Sirine Arfa, Bernhard Vogginger, Chen Liu +3
Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware. Their ability to process eve…
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer +2
Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved th…
STREAM: A Universal State-Space Model for Sparse Geometric Data
Mark Schöne, Yash Bhisikar, Karan Bania +4
Handling sparse and unstructured geometric data, such as point clouds or event-based vision, is a pressing challenge in the field of machine vision. Recently, sequence models such…
Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models
Mark Schöne, Neeraj Mohan Sushma, Jingyue Zhuge +3
Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other v…