S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices
Structured State Space Models (SSMs), including the S4 and S4D architectures, have recently emerged as powerful alternatives to attention-based models for capturing long-range dependencies in sequential data. Despite their strong empirical performance, deploying these models in time- and resource-constrained settings remains challenging due to their computational and memory demands. In this paper, we propose a novel incremental, operator-level pruning approach for S4- and S4D-based models that significantly reduces inference cost while preserving predictive performance. To the best of our knowledge, this is the first work to systematically investigate structured operator pruning for SSMs....
| Authors | Marco Deano, Filippo Ziche, Nicola Bombieri |
|---|---|
| Primary category | cs.LG |
| Published | 16 June 2026 |
| Last updated at source | 8 October 2026 |
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