Formal Algorithms for Model Efficiency
This work provides a foundational formalism for researchers in model efficiency, but it is incremental as it unifies existing methods rather than introducing a new paradigm.
The paper tackles the problem of unifying diverse model efficiency techniques in deep learning by introducing the Knob-Meter-Rule (KMR) framework, which abstracts methods like pruning and quantization into a consistent formalism to enable systematic composition and optimization, though no concrete performance numbers are provided.
We introduce the Knob-Meter-Rule (KMR) framework, a unified formalism for representing and reasoning about model efficiency techniques in deep learning. By abstracting diverse methods, including pruning, quantization, knowledge distillation, and parameter-efficient architectures, into a consistent set of controllable knobs, deterministic rules, and measurable meters, KMR provides a mathematically precise and modular perspective on efficiency optimization. The framework enables systematic composition of multiple techniques, flexible policy-driven application, and iterative budgeted optimization through the Budgeted-KMR algorithm. We demonstrate how well-known efficiency methods can be instantiated as KMR triples and present concise algorithmic templates for each. The framework highlights underlying relationships between methods, facilitates hybrid pipelines, and lays the foundation for future research in automated policy learning, dynamic adaptation, and theoretical analysis of cost-quality trade-offs. Overall, KMR offers both a conceptual and practical tool for unifying and advancing model efficiency research.