A new study reveals that the energy consumption of vision-language models (VLMs) on edge devices is primarily driven by the amount of output generated, rather than the complexity of the visual input. Researchers found that generating each output token takes significantly longer and consumes more energy than processing input tokens. This suggests that efforts to reduce VLM energy usage should focus on controlling output length, as even eliminating visual input processing yields minimal energy savings. AI
IMPACT Focusing on output length control for VLMs could significantly reduce energy consumption on edge devices.
RANK_REASON The cluster contains a research paper detailing findings on VLM energy consumption.
- CNNs
- compound shrinking
- dynamic image cropping
- dynamic inference framework
- EdgeCompress
- ImageNet-1K
- ResNet-50
- Hugging Face
- NVIDIA Jetson Orin NX 16GB
- Nvidia RTX 3070
- vision-language model
- Vision--Language Models
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