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Found 4 papers, 1 papers with code
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
This development is accompanied by a rapid growth of the required computational demands for larger models and more data.
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
In this work, we demonstrate the first-ever implementation of a language model on a neuromorphic device - specifically the SpiNNaker 2 chip - based on a recently published event-based architecture called the EGRU.
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Yet, sparse activations, while omnipresent in both biological neural networks and deep learning systems, have not been fully utilized as a compression technique in deep learning.
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real-world application requirements.
Ranked #2 on
Gesture Recognition
on DVS128 Gesture
(using extra training data)
