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Chinese team develops intelligent vision sensor that turns light signals into AI-ready tokens, reducing use of energy_我的网站

A |     　　讯 据国家统计局消息，1—7月份，随着新质生产力快速发展、新旧动能平稳接续转换，技术创新与产业升级等相关领域投资保持良好增长态势，带动投资结构不断向新向优。

B | 　　一、高技术产业投资增速加快　　新兴产业加快培育壮大，高技术产业投资增速加快。

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    The new ultra-low-power intelligent vision sensor chip "LightTok" developed by a research team from Nanjing University. Photo: from Science and Technology Daily
A Chinese research team from Nanjing University has developed a new ultra-low-power intelligent vision sensor chip, dubbed "LightTok," that can convert light signals into tokens within the sensor, significantly reducing the high energy consumption caused by frequent transfers of massive amounts of redundant data, the principal investigator told the Global Times.
According to a release from the Institute of Brain-Inspired Intelligence of Nanjing University, tokens generated by the LightTok chip can be directly fed into a Transformer encoder for image recognition.
"Our design idea was to move token generation onto the sensor itself, allowing the chip to directly produce tokens that AI models can process once light reaches the sensor," Miao Feng, director of the Institute of Brain-Inspired Intelligence at Nanjing University, told the Global Times on Thursday. "These tokens contain complete image information."
Physical AI refers to intelligent systems capable of autonomously perceiving, reasoning, acting and receiving feedback in the real world, representing a key pathway for AI to move from the digital realm into the physical world. Vision-based physical AI systems powered by large AI models need to convert visual information from real-world environments into tokens that can be processed by AI models before feeding these tokens into Transformers for subsequent tasks.
In traditional visual perception pipelines, light signals must go through multiple stages, including image sensing, analog-to-digital conversion, data buffering and transfer, digital image patching and embedding, before being transformed into tokens that AI models can process. The frequent transfer of massive amounts of redundant data has resulted in high energy consumption at the edge, according to a report by Science and Technology Daily.
The LightTok chip directly addresses a key challenge in physical AI hardware: how to efficiently acquire and tokenize visual information from the physical world with low energy consumption, Miao said.
The LightTok chip consists of a photosensitive memory array and peripheral circuits. The team built the array based on single-layer molybdenum disulfide (MoS₂) floating-gate phototransistors, with each pixel capable of sensing light, storing information and performing analog computing. By processing optical information directly within the chip, the device can convert captured visual signals into tokens for AI models, according to the research team.
The current LightTok prototype has a resolution of 32×32 pixels, or 1,024 photosensitive pixels, which is still smaller than that of smartphone cameras and industrial imaging systems. However, Miao said the technology is compatible with CMOS manufacturing processes and can be scaled up. With wafer-level growth of molybdenum disulfide materials, the chip could potentially achieve a scale comparable to existing imaging devices.
Miao said the chip also draws inspiration from the information-processing mechanism of human vision. Similar to how the retina extracts key visual information before transmitting it to the brain, the chip also aims to process visual information at an early stage. 
The research was conducted in collaboration with another research team from the National University of Singapore. The findings were published on Wednesday in Nature Sensors, an internationally renowned journal in the field of sensing technology, according to the release.
Potential applications for LightTok include drone systems, autonomous remote sensing and small-scale embodied AI systems, Miao said. In these scenarios, devices need to continuously detect, understand and track targets, generating massive amounts of visual data. By reducing the energy required for visual processing, the technology could extend the operating time of drones, satellites and small robots with limited power supplies, the expert said.    
。1—7月份，高技术产业投资同比增长5.0%，增速比上半年加快0.4个百分点，拉动全部投资增长0.5个百分点。　　1—7月份，高技术制造业投资同比增长3.3%，增速比上半年加快0.1个百分点。其中，电子电路制造业投资增长57.7%，增速加快2.1个百分点；集成电路制造业投资增长11.5%，增速加快2.7个百分点；锂离子电池制造业投资增长23.0%；电子专用材料制造业投资增长9.4%。　　1—7月份，高技术服务业投资同比增长8.4%，增速比上半年加快0.9个百分点。

D | 在人工智能快速发展的带动下，信息服务业投资增长19.2%，增速加快3.7个百分点。

E | 　　二、知识产权产品投资平稳较快增长　　创新驱动发展不断深化，知识产权产品投资力度持续加大。

F | 1—7月份，全国知识产权产品投资同比增长9.1%；占全部投资的比重为14.8%，比上年同期提高2.1个百分点；拉动全部投资增长1.2个百分点。　　三、设备购置投资增速提高　　“两新”政策效应持续显现，人工智能算力建设布局持续加快，带动设备购置投资保持较快增长。1—7月份，设备工器具购置投资同比增长9.0%，增速比上半年加快0.9个百分点；拉动全部投资增长1.5个百分点；占全部投资的比重为19.1%，比上年同期提高2.8个百分点。　　四、重点领域基础设施投资增势较好　　“两重”项目建设扎实推进，以“六张网”为代表的现代化基础设施体系建设加快推进，相关领域投资保持良好增势。1—7月份，互联网和相关服务业投资同比增长41.3%，增速比上半年加快1.4个百分点；航空运输业投资增长15.7%，增速加快4.7个百分点；水上运输业投资增长16.2%；电力供应业投资增长16.0%。　　五、工业投资结构持续优化　　现代化产业体系建设稳步推进，传统产业转型升级加快，工业投资结构向优。1—7月份，采矿业投资同比增长3.3%，拉动全部工业投资增长0.2个百分点。装备制造业投资增长1.1%，拉动全部工业投资增长0.4个百分点。其中，铁路、船舶、航空航天和其他运输设备制造业投资增长18.7%；计算机、通信和其他电子设备制造业投资增长7.8%，增速比上半年加快1.3个百分点。　　下阶段，要深入贯彻党中央、国务院决策部署，加快财政支出和债券资金使用进度，有力推进“两重”建设和“两新”工作，扎实推进“六张网”规划建设，全链条推动新兴支柱产业规模化发展，加快现代化产业体系建设，坚持投资于物和投资于人协同发力，进一步优化提升投资结构和效益，更好发挥有效投资对优化供给结构的关键作用。版权申明：凡注有“”或电头为“”的稿件，均为独家版权所有，未经许可不得转载或镜像；授权转载必须注明来源为“”，并保留“”的电头。    。

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Published on:04:02:19