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cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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)…