EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Unified Compression and Adaptive Layer Voting

This is the authors' abstract. We don't add key points for this paper.

Efficient adaption of large language models (LLMs) on edge devices is essential for applications requiring continuous and privacy-preserving adaptation and inference. However, existing tuning techniques fall short because of the high computation and memory overhead. To this end, we introduce a computation- and memory-efficient LLM tuning framework, called Edge-LLM, to facilitate affordable and effective LLM adaptation on edge devices. Specifically, Edge-LLM features three core components: (1) a layer-wise unified compression (LUC) technique to reduce the computation overhead by generating layer-wise pruning sparsity and quantization bit-width policies, (2) an adaptive layer tuning and voting scheme to reduce the memory overhead by reducing the backpropagation depth, and (3) a complementary hardware scheduling strategy to handle the irregular computation patterns introduced by LUC and adaptive layer tuning, thereby achieving improved real hardware efficiency. Extensive experiments demonstrate that Edge-LLM achieves on-device adaptation with comparable task accuracy as vanilla tuning methods with a 2.92× speed up and a 4× reduction in memory overhead. Our code is available at https://github.com/GATECH-EIC/Edge-LLM