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Updated: Feb 12, 2026

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Thermal Measurement Techniques in Analytical Microfluidic Devices
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一个按需的神经形态视觉系统,由多范式的神经形态设备和从设备到系统层面设计的层次重配置能力启用
Biyi Jiang1, Jiayi Xu1, Liang Ran2
1School of Microelectronics, Southern University of Science and Technology, Shenzhen, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|February 11, 2026
概括
这项研究介绍了一种超可重新配置的智能视觉系统,弥合了灵活性和功率效率之间的差距. 新系统在动态和静态场景中实现了卓越的性能,超过了当前技术.
科学领域:
- 神经形态工程的神经形态工程
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 当前的智能视觉系统缺乏动态场景所需的灵活性和功率效率.
- 现有的系统依赖于刚性架构和功能有限的设备,阻碍了适应性.
- 生物视觉系统为高智能,效率和灵活性提供了一个基准.
研究的目的:
- 开发一个按需可超重配置的智能视觉系统.
- 通过实现功能之间无切换来弥合神经形态差距.
- 在视觉系统中实现并发的高智能,效率和灵活性.
主要方法:
- 开发了一种多范式设备阵列,能够在尖端,非尖端,神经形态成像 (NI) 和人工智能 (AI) 模式之间切换.
- 实现了可重新配置的电路和架构设计,用于按需分配资源.
- 在设备,单元,数组和系统层面证明了真正的重新配置性.
主要成果:
- 实现了高达52.6 TOPS/W (NI中心) 和75.5 TOPS/W (NI/AI混合) 的卓越功率效率.
- 在NI和AI功能之间展示了对智能成像和识别的按需资源分配.
- 启用了尖端 (动态) 和非尖端 (静态) 模式之间的无过渡.
结论:
- 开发的系统为智能视觉提供了前所未有的重新配置性和功率效率.
- 这项工作代表了模仿生物视觉系统能力的重大进步.
- 该系统为动态和不可预测的视觉处理任务提供了灵活和高效的解决方案.
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