arXiv:2411.09816v4 Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices. Among existing compression approaches, cross-layer parameter sharing remains relatively unexplored for transformer models. In this paper, we introduce Fine-grained Parameter Sharing (FiPS), a unified framework for compressing transformer Multi-Layer Perceptrons (MLPs) that combines cross-block parameter sharing, low-rank factorization, and sparsity in a single optimization. FiPS concatenates MLP we
The continuous growth in size and complexity of neural networks necessitates new compression techniques to enable broader deployment, particularly on resource-constrained devices, which aligns with ongoing research trends seeking efficiency gains.
This research introduces a novel, unified framework for compressing large language models, potentially reducing their computational footprint and expanding their applicability to edge devices and environments with limited resources.
The ability to significantly compress transformer MLPs through fine-grained parameter sharing, low-rank factorization, and sparsity offers a new pathway for deploying powerful AI models in previously inaccessible settings.
- · Edge AI device manufacturers
- · Developers of mobile AI applications
- · Organizations with limited compute budgets
- · AI model deployers in remote or constrained environments
- · Providers of exclusively cloud-based AI solutions
- · Companies reliant on expensive, high-end inference hardware
Widespread adoption of transformer models on edge devices becomes more feasible.
Reduced operational costs for AI inference, broadening access and innovation.
New classes of AI applications emerge that leverage omnipresent, low-resource intelligent agents.
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Read at arXiv cs.LG