  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.
