Exploring Liquid Neural Networks on Loihi-2
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909273898876928 |
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| author | Pawlak, Wiktoria Agata Isik, Murat Le, Dexter Dikmen, Ismail Can |
| author_facet | Pawlak, Wiktoria Agata Isik, Murat Le, Dexter Dikmen, Ismail Can |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Liquid Neural Networks on Loihi-2 Pawlak, Wiktoria Agata Isik, Murat Le, Dexter Dikmen, Ismail Can Emerging Technologies Hardware Architecture 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. |
| title | Exploring Liquid Neural Networks on Loihi-2 |
| topic | Emerging Technologies Hardware Architecture |
| url | https://arxiv.org/abs/2407.20590 |