@inproceedings{d5f7612cb8af4a7b9b6ac5fd1f0b8fc9,
title = "Towards ARSPI-Net: Development of an efficient hybrid deep learning framework",
abstract = "In this paper, we implement and experiment with a portion of our overall hybrid deep learning framework, An Affective hYbrid SPIking Neural Network [ARSPI-NET]. In order to motivate and show the usage of liquid state machines as an efficient, feature extraction framework we perform experiments on connection architecture as well as neuron model and their effect on overall classification performance. We perform our initial experimentation on the MNIST dataset and achieve a 87\% classification using a liquid state machine and logistic regression classifier. Our results suggest that our framework can compare with current models in terms of accuracy, however, we outperform traditional deep learning methods in terms of energy consumption and the potential to move to energy-efficient neuromorphic platforms. In addition, our framework has the advantage of being more interpretable, as it allows us to model the spatiotemporal dynamics of signals through the usage of a liquid reservoir and an interpretable readout vector. This paper sets precedence for future experimentation and development of ARSPI-Net as a hybrid deep learning framework.",
keywords = "Affective Computing, Deep Learning, edge computing, energy efficiency neural networks, Neuomorphic, Recurrent Neural Networks, Spiking Neural Networks, TinyML",
author = "Andrew Lane and Wendy Tang and Brady Nelson",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2023 ; Conference date: 05-05-2023",
year = "2023",
doi = "10.1109/LISAT58403.2023.10179592",
language = "English",
series = "2023 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2023",
}