对于法学士来说,下一步是什么? 通过光子芯片推动下一代人工智能计算硬件的界限
Renjie Li1,2, Qi Xin1, Wenjie Wei3
1School of Science and Engineering, Guangdong Key Laboratory of Optoelectronic Materials and Chips, Shenzhen Key Lab of Semiconductor Lasers, The Chinese University of Hong Kong, Shenzhen, China.
Nanophotonics (Berlin, Germany)
|November 10, 2025
概括
新兴的光子硬件为大型语言模型 (LLM) 提供超快,高能效的计算. 虽然有希望,但需要在内存和存储方面的突破来扩展这些人工智能系统.
科学领域:
- 光子学和人工智能 (AI) 的发展
- 神经形态计算是一种神经形态计算.
- 先进的材料科学科学 材料科学
背景情况:
- 大型语言模型 (LLM) 需要大量的计算资源,使当前的硬件承受压力.
- 传统的·诺伊曼架构面临AI工作负载的功率和速度限制.
- 生成性AI的快速发展需要新的计算范式.
研究的目的:
- 审查优化为下一代生成AI计算的新兴光子硬件.
- 分析光子组件和人工智能算法的集成,用于LLMs.
- 确定大规模AI模型的光子系统扩展的挑战和机会.
主要方法:
- 综合光子神经网络架构的调查 (例如,马赫-泽恩德干扰仪网格,微环共振器).
- 检查替代的神经形态装置,包括2D材料和自旋-光子突触.
- 分析基于变压器的LLM架构及其对光子系统的映射.
主要成果:
- 光子计算系统显示了与电子处理器相比,吞吐量和能源效率的数量级改进的潜力.
- 集成的光子神经网络执行超快的矩阵运算.
- 像石墨烯和TMDCs这样的新材料增强了可调节调节器和突触元件的光子平台.
结论:
- 光子计算为高效,高性能的人工智能硬件提供了一个可行的途径.
- 对于大规模的LLM来说,关键的挑战仍然在于内存,存储和长文本处理.
- 对系统集成和光子组件开发的进一步研究对于实现人工智能硬件的全部潜力至关重要.
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