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.

翻译：本研究探讨了液态神经网络（LNNs）及其在神经形态硬件平台上的部署。文章深入分析了液态状态机（LSMs），并探索了LNN架构在神经形态系统中的适应性，重点阐述了其理论基础与实际应用。我们通过在先进的神经形态硬件平台上实现液态神经网络（LNNs），提出了一种用于CIFAR-10数据集图像分类的开创性方法。我们基于Loihi-2 ASIC的架构展现了卓越的性能，实现了91.3%的显著准确率，同时每帧仅消耗213微焦耳。这些结果凸显了LNNs在推进神经形态计算方面的巨大潜力，并在效率与准确性方面为该领域确立了新的基准。