支持微组的并行自我校准光学卷积流处理器
Jiajia Wang1, Xingyuan Xu2, Xiaotian Zhu3
1State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China.
Light, science & applications
|March 5, 2026
概括
本研究介绍了一种新的光学卷积流处理器 (OCSP),用于更快,更节能的人工智能硬件. 这款支持微组合的处理器实现了4个TOPS,满足了数据中心的需求.
科学领域:
- 光子学和神经形态计算
- 数据中心硬件架构 数据中心硬件架构
背景情况:
- 云计算和人工智能的指数式增长需要高带宽,节能的数据中心硬件.
- 摩尔定律的局限性推动了寻找替代方案的研究,比如用于超高速和低能耗的光学神经形态计算.
研究的目的:
- 提出和验证一个支持微组的并行光学卷积流处理器 (OCSP).
- 展示一个实用的解决方案,将光子计算集成到用于AI工作负载的数据中心互连中.
主要方法:
- 开发了一个利用时间,空间和波长三维多重复的OCSP.
- 实施了强大的自我校准机制,用于准确的光学相位校准.
- 采用时空交错的被动周期干扰架构与波长分割复杂化.
主要成果:
- 实现的数据速率为50 GBaud或更高.
- 达到高达每秒4万亿次运算 (TOPS) 的卷积计算速度.
- 对并行图像特征提取和识别任务进行实验验证的性能.
结论:
- OCSP为数据中心的AI工作负载提供了一个可扩展,低延迟的解决方案.
- 这项技术为将光子计算单元集成到现有数据中心基础设施中提供了一条实用的途径.
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