Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests
arXiv:2607.05457v1 Announce Type: cross Abstract: Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantised representations without explicitly characterising the dynamical role of internal states. This paper proposes a controllability-observability framework for empirical state-order reduction of deep neural networks. By viewing a trained network as a depth-indexed nonlinear dynamical system, we construct data-driven reachability, observability, and balanced Gramians from hidden-state snapshots and ou
The proliferation of increasingly large and complex deep neural networks creates an urgent need for more efficient compression techniques that move beyond superficial weight pruning. This research leverages control theory, a mature field, to address a contemporary AI scaling challenge.
Efficient compression of deep neural networks can drastically reduce the computational resources needed for deployment, enabling broader accessibility and more sustainable AI development. It could also accelerate research by making models easier to experiment with and deploy.
This research introduces a novel theoretical framework for DNN compression rooted in dynamical systems, potentially shifting the paradigm from heuristic weight manipulation to principled state-order reduction based on controllability and observability. It could lead to more robust and explainable compression.
- · AI hardware manufacturers (for specialized compression chips)
- · Cloud AI providers (reduced operational costs)
- · Edge AI developers (deploying complex models on constrained devices)
- · AI-as-a-Service companies
- · Companies reliant on brute-force large model deployment
- · Traditional compression algorithm developers (if this approach proves superior)
- · Energy producers (if AI compute demands are significantly reduced)
Reduced computational and energy footprint for deploying large AI models.
Accelerated adoption of AI in resource-constrained environments like mobile and IoT devices, and potentially in sovereign AI initiatives.
Increased competition in AI model deployment due to lower entry barriers, potentially democratizing access to powerful AI capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI