BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression
arXiv:2509.25136v3 Announce Type: replace Abstract: Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whitening-based theory typically makes an implicit full-rank assumption on activations. We introduce a layer representation framework that extends activation-aware factorization beyond linear layers, including standard and grouped convolutions. Within this framework, our whitening-based formulation is more general than prior ones, naturally covering rank-deficient activations, and yields an optimal lo
This research addresses the growing need for more efficient AI models as their complexity and computational demands increase, making compression techniques crucial for wider deployment.
Sophisticated readers should care because effective model compression techniques directly impact the accessibility, cost, and energy efficiency of deploying advanced AI models, especially on edge devices or with limited resources.
This advancement changes the landscape of model compression by extending activation-aware factorization to non-linear layers, potentially enabling broader and more effective application across various AI architectures.
- · AI hardware manufacturers
- · Edge AI developers
- · AI service providers
- · Startups developing frugal AI solutions
- · Companies relying solely on large, uncompressed models
- · Legacy AI model architectures
More compact and efficient AI models will become feasible for deployment in resource-constrained environments.
This could accelerate the adoption of AI in new applications where computational power or energy consumption was previously a barrier.
The reduced computational burden may contribute to a slight deceleration in the exponential growth of AI's energy footprint, impacting the compute-energy nexus.
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Read at arXiv cs.LG