<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>AI硬件 on Deep Research</title>
    <link>https://dailydigest.aabot.us/zh/tags/ai%E7%A1%AC%E4%BB%B6/</link>
    <description>Recent content in AI硬件 on Deep Research</description>
    <generator>Hugo</generator>
    <language>zh-CN</language>
    <lastBuildDate>Fri, 15 May 2026 04:00:00 +0000</lastBuildDate>
    <atom:link href="https://dailydigest.aabot.us/zh/tags/ai%E7%A1%AC%E4%BB%B6/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>神经形态计算在机器人导航中的应用：为何二十年的承诺终于成为现实</title>
      <link>https://dailydigest.aabot.us/zh/posts/2026-05-15-neuromorphic-computing-for-robot-navigation-spiking-neural-networks-enable-100x-lower-power-consumption-in-autonomous-drones/</link>
      <pubDate>Fri, 15 May 2026 04:00:00 +0000</pubDate>
      <guid>https://dailydigest.aabot.us/zh/posts/2026-05-15-neuromorphic-computing-for-robot-navigation-spiking-neural-networks-enable-100x-lower-power-consumption-in-autonomous-drones/</guid>
      <description>经过数十年的未实现承诺，神经形态计算终于解决了自主机器人导航问题，功耗比传统AI降低了100倍。这一突破源于解决了历史上阻碍部署的三个关键障碍：缺乏适用于脉冲神经网络的训练算法、芯片间扩展性差以及软件工具链有限。</description>
    </item>
    <item>
      <title>超越石墨烯：过渡金属二硫族化合物重塑人工智能硬件与量子计算</title>
      <link>https://dailydigest.aabot.us/zh/posts/2026-04-22-2d-materials-beyond-graphene-transition-metal-dichalcogenides/</link>
      <pubDate>Wed, 22 Apr 2026 04:00:00 +0000</pubDate>
      <guid>https://dailydigest.aabot.us/zh/posts/2026-04-22-2d-materials-beyond-graphene-transition-metal-dichalcogenides/</guid>
      <description>虽然石墨烯在早期二维材料研究中备受关注，但过渡金属二硫族化合物如MoS2如今正在推动从神经形态AI芯片到室温量子处理器的突破性应用。与石墨烯零带隙限制不同，TMDs提供1-3 eV可调半导体特性，能够直接集成到数字逻辑和量子器件中，无需困扰石墨烯商业化的复杂带隙工程。</description>
    </item>
  </channel>
</rss>
