Hypervision: An on-chip hyperspectral microsystem for online video-rate computational imaging.

Authors

Liheng Bian, Qinghao Meng, Lianjie Li, Zhen Wang, Yibo Feng, Xuan Peng, Jiajun Zhao, Jingyi Wang, Zhu Yang, Jun Zhang

Published in

Science (New York, N.Y.). Volume 393. Issue 6814. Pages 888-894. Aug 27, 2026. Epub Aug 27, 2026.

Abstract

In this work, we tackled the long-standing challenge of the massive computation for hyperspectral imaging that is required to reconstruct and process large-volume spatial-spectral data cubes. Specifically, we designed a hardware accelerator, fabricated as a neural processing unit (NPU) capable of 9.3 tera operations per second at 16-bit integer (INT16), alongside a topology-aware structured pruning strategy for a lightweight reconstruction network. Through integration with our HyperspecI sensor, we demonstrate a fully standalone visible-near-infrared hyperspectral microsystem (~950 grams) that requires neither external power nor computing resources. The microsystem achieved real-time hyperspectral imaging at 32.9 frames per second (512×512, 61 channels) or 24.6 frames per second (1024×1024, 16 channels) and consumed only ~25.3 watts (367 giga-operations per second per watt). Application demonstrations in intelligent driving and air-to-ground monitoring highlight its practical potential advancing computational hyperspectral imaging from offline processing to integrated online perception.

PMID:
42658948
Bibliographic data and abstract were imported from PubMed on 28 Aug 2026.

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