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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01257 |
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| _version_ | 1866913771906138112 |
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| author | Chakraborty, Biswadeep Mukhopadhyay, Saibal |
| author_facet | Chakraborty, Biswadeep Mukhopadhyay, Saibal |
| contents | We propose \textbf{FLAMES (Fast Long-range Adaptive Memory for Event-based Systems)}, a novel hybrid framework integrating structured state-space dynamics with event-driven computation. At its core, the \textit{Spike-Aware HiPPO (SA-HiPPO) mechanism} dynamically adjusts memory retention based on inter-spike intervals, preserving both short- and long-range dependencies. To maintain computational efficiency, we introduce a normal-plus-low-rank (NPLR) decomposition, reducing complexity from $\mathcal{O}(N^2)$ to $\mathcal{O}(Nr)$. FLAMES achieves state-of-the-art results on the Long Range Arena benchmark and event datasets like HAR-DVS and Celex-HAR. By bridging neuromorphic computing and structured sequence modeling, FLAMES enables scalable long-range reasoning in event-driven systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning Chakraborty, Biswadeep Mukhopadhyay, Saibal Machine Learning We propose \textbf{FLAMES (Fast Long-range Adaptive Memory for Event-based Systems)}, a novel hybrid framework integrating structured state-space dynamics with event-driven computation. At its core, the \textit{Spike-Aware HiPPO (SA-HiPPO) mechanism} dynamically adjusts memory retention based on inter-spike intervals, preserving both short- and long-range dependencies. To maintain computational efficiency, we introduce a normal-plus-low-rank (NPLR) decomposition, reducing complexity from $\mathcal{O}(N^2)$ to $\mathcal{O}(Nr)$. FLAMES achieves state-of-the-art results on the Long Range Arena benchmark and event datasets like HAR-DVS and Celex-HAR. By bridging neuromorphic computing and structured sequence modeling, FLAMES enables scalable long-range reasoning in event-driven systems. |
| title | FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.01257 |