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Tiny but Mighty: A Software-Hardware Co-Design Approach for Efficient Multimodal Inference on Battery-Powered Small Devices
arXiv – CS AI|Yilong Li, Shuai Zhang, Yijing Zeng, Hao Zhang, Xinmiao Xiong, Jingyu Liu, Pan Hu, Suman Banerjee||4 views
🤖AI Summary
Researchers developed NANOMIND, a software-hardware framework that optimizes Large Multimodal Models for battery-powered devices by breaking them into modular components and mapping each to optimal accelerators. The system achieves 42.3% energy reduction and enables 20.8 hours of operation running LLaVA-OneVision on a compact device without network connectivity.
Key Takeaways
- →NANOMIND framework breaks Large Multimodal Models into modular 'bricks' that run on different accelerators (NPUs, GPUs, DSPs) for optimal efficiency.
- →The system reduces energy consumption by 42.3% and GPU memory usage by 11.2% compared to existing implementations.
- →A battery-powered prototype can run LLaVA-OneVision with camera functionality for nearly 21 hours continuously.
- →The framework enables completely offline AI inference without requiring network connectivity.
- →Module-level dynamic offloading and token-aware buffer management eliminate CPU bottlenecks and reduce memory waste.
#multimodal-ai#edge-computing#hardware-optimization#battery-efficiency#offline-ai#nanomind#llava#mobile-ai#accelerators
Read Original →via arXiv – CS AI
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