18 citations · 37 across the 8 of their papers we have counts for
8 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…
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…
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer +3
Activity and parameter sparsity are two standard methods of making neural networks computationally more efficient. Event-based architectures such as spiking neural networks (SNNs)…
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…
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
Khaleelulla Khan Nazeer, Mark Schöne, Rishav Mukherji +4
As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential…
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
Hector A. Gonzalez, Jiaxin Huang, Florian Kelber +11
The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This de…
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…
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
Anand Subramoney, Khaleelulla Khan Nazeer, Mark Schöne +2
Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, t…