arXiv:2510.14542v2 Announce Type: replace-cross Abstract: We study deep state-space models (Deep SSMs) that contain linear quadratic-output (LQO) systems as internal blocks and present a compression method with a provable output error guarantee. We first derive an upper bound on the output error between two Deep SSMs and show that the bound can be expressed in terms of the $h^2$-error norms between the layerwise LQO systems. In particular, we show that reducing the $h^2$ approximation errors of the LQO systems placed in shallow layers is effective in reducing the derived upper bound on the out
The proliferation of deep learning models has led to increased demand for efficient deployment and reduced computational overhead, making model compression a critical research area.
This research provides a method for compressing complex deep state-space models with provable error guarantees, which is crucial for deploying AI models in resource-constrained or real-time environments.
The ability to compress deep models more reliably and efficiently will accelerate the adoption of advanced AI in various applications, particularly those requiring embedded or edge computing.
- · Edge AI developers
- · Hardware manufacturers for AI
- · Providers of efficient AI services
- · SaaS companies leveraging AI
- · Providers of inefficient, large AI models
- · Traditional hardware-centric scaling solutions
Deep AI models can be deployed more widely and cost-effectively due to reduced computational requirements.
This efficiency gain could lower the barrier to entry for developing and deploying sophisticated AI applications, fostering innovation in specialized domains.
The widespread deployment of compressed, high-performance AI could lead to a new wave of autonomous systems and smart infrastructure with real-time decision-making capabilities.
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Read at arXiv cs.LG