S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices
arXiv:2606.18096v1 Announce Type: cross Abstract: 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 preservi
The proliferation of advanced AI models demands efficient deployment on diverse hardware, driving research into optimization techniques like pruning for resource-constrained environments.
This development addresses a critical bottleneck in deploying powerful AI models, enabling wider adoption and new applications in edge computing and embedded systems.
The ability to significantly reduce the computational and memory demands of Structured State Space Models (SSMs) makes them more viable for real-world, resource-limited deployments.
- · Edge AI device manufacturers
- · Embedded systems developers
- · AI model deployers
- · AI research in efficiency
- · Developers solely reliant on large, unoptimized models
Increased accessibility and deployment of advanced AI models on consumer and industrial edge devices.
Accelerated innovation in applications requiring low-power, high-performance AI at the device level.
Potential for new business models and services built around ubiquitous, efficient AI inference on diverse hardware.
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Read at arXiv cs.AI