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arXiv · 2407.20590

Exploring Liquid Neural Networks on Loihi-2

Wiktoria Agata Pawlak·Murat Isik·Dexter Le·Ismail Can Dikmen
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Abstract

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.

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Wiktoria Agata Pawlak, Murat Isik, Dexter Le, Ismail Can Dikmen. 2024-07-30. Exploring Liquid Neural Networks on Loihi-2. https://arxiv.org/abs/2407.20590

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