Running LLMs on devices like smartphones has become a key catalyst towards privacy-preserving mobile AI. In state-of-the-art frameworks, the attention operator falls back from the dedicated NPU to the public CPU/GPU due to its sensitivity to quantization. Such a fallback leads to hampered user experience and extra system scheduling complexity. To realize NPU-centric LLM inference, this paper presents shadowNPU, a system-algorithm codesigned sparse attention module with minimal reliance on CPU/GPU by only sparsely computing a very small number of tokens. The key idea is to hide the overhead of estimating the important tokens by offloading it to NPU. On top of this, it further incorporates insightful techniques including NPU compute graph bucketing, head-wise NPU-CPU/GPU pipeline and per-head fine-grained sparsity ratio to achieve high accuracy and efficiency. Compared to design alternatives, shadowNPU achieves the best performance with strictly limited CPU/GPU resource; it requires much less CPU/GPU resource to achieve on-par performance of SoTA frameworks.