Nanomind decomposes LMMs into modular bricks mapped to heterogeneous accelerators with TABM zero-copy transfers, fused low-bit kernels, and a battery-aware scheduler, cutting energy 42.3% and enabling 18.8-hour runtime on a 2000 mAh battery for LLaVA-OneVision-Qwen2-0.5B.
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee
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Tiny but Mighty: A Software-Hardware Co-Design Approach for Efficient Multimodal Inference on Battery-Powered Small Devices
Nanomind decomposes LMMs into modular bricks mapped to heterogeneous accelerators with TABM zero-copy transfers, fused low-bit kernels, and a battery-aware scheduler, cutting energy 42.3% and enabling 18.8-hour runtime on a 2000 mAh battery for LLaVA-OneVision-Qwen2-0.5B.