arXiv:2606.08565v1 Announce Type: new Abstract: Tensor networks provide efficient representations for compressing large neural networks. By carefully designing shapes and topologies, they can significantly reduce memory and computational costs. However, identifying implicit low-rank structures in large foundation models remains challenging due to their enormous scale and un-structured weight distributions. We propose an adaptive tensorization method that discovers inherent low-rank structure in a target tensor by index ordering. Experiments on weight and KV-cache compression demonstrate improv
This research is emerging as the scale of Large Language Models (LLMs) continues to grow exponentially, exacerbating existing memory and computational challenges.
Efficient compression techniques for LLMs are critical for their ubiquitous deployment, especially in resource-constrained environments or for edge AI applications.
The ability to identify and leverage implicit low-rank structures in LLMs offers a new pathway to significantly reduce their memory footprint and computational load.
- · AI hardware manufacturers (specialized for tensor networks)
- · Cloud AI providers (reduced operational costs)
- · Edge AI developers
- · Developers of large language models
- · Companies reliant on brute-force scaling of LLMs
- · Existing LLM compression techniques that are less efficient
More cost-effective and energy-efficient deployment of large AI models becomes possible.
Broader accessibility and wider application of advanced AI models across various industries, including embedded systems and on-device AI.
Accelerated development of even larger and more complex AI models, pushing the boundaries of what is computationally feasible.
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Read at arXiv cs.LG