Exploring Liquid Neural Networks on Loihi-2
<|reference_start|>Exploring Liquid Neural Networks on Loihi-2: This study investigates the realm of liquid neural networks (LNNs) and their deployment on neuromorphic hardware platforms. It provides an in-depth analysis of Liquid State Machines (LSMs) and explores the adaptation of LNN architectures to neuromorphic systems, highlighting the theoretical foundations and practical applications. We introduce a pioneering approach to image classification on the CIFAR-10 dataset by implementing Liquid Neural Networks (LNNs) on state-of-the-art neuromorphic hardware platforms. Our Loihi-2 ASIC-based architecture demonstrates exceptional performance, achieving a remarkable accuracy of 91.3% while consuming only 213 microJoules per frame. These results underscore the substantial potential of LNNs for advancing neuromorphic computing and establish a new benchmark for the field in terms of both efficiency and accuracy.<|reference_end|>
@article{pawlak2024exploring,
    title={Exploring Liquid Neural Networks on Loihi-2},
    author={Wiktoria Agata Pawlak, Murat Isik, Dexter Le, Ismail Can Dikmen},
    journal={arXiv preprint arXiv:2407.20590},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20590},
    primaryClass={cs.ET cs.AR}
}