标题： 光子存内计算的系统级性能建模 显示英文标题

标题： System-Level Performance Modeling of Photonic In-Memory Computing

摘要： 光子内存计算是一种高速、低能耗的替代传统基于晶体管的数字计算的方法，它利用了高光子工作频率和带宽。 在本工作中，我们为光子内存计算开发了一个全面的系统级性能模型，捕捉了外部存储访问和光电转换等关键延迟源的影响。 我们在一系列工作负载上进行算法到硬件的映射，包括Sod冲击波管问题、矩阵化张量乘以Khatri-Rao积（MTTKRP）和Vlasov-Maxwell方程，以评估这些延迟如何影响实际的高性能计算工作负载。 我们的性能模型显示，尽管考虑了系统开销，一个使用GlobalFoundries标准硅光子工艺制造的紧凑型1x256位单波长光子SRAM阵列，在Sod冲击波管问题、MTTKRP和Vlasov-Maxwell方程上的性能分别达到1.5 TOPS、0.9 TOPS和1.3 TOPS，平均能效为2.5 TOPS/W。 显示英文摘要

摘要： Photonic in-memory computing is a high-speed, low-energy alternative to traditional transistor-based digital computing that utilizes high photonic operating frequencies and bandwidths. In this work, we develop a comprehensive system-level performance model for photonic in-memory computing, capturing the effects of key latency sources such as external memory access and opto-electronic conversion. We perform algorithm-to-hardware mapping across a range of workloads, including the Sod shock tube problem, Matricized Tensor Times Khatri-Rao Product (MTTKRP), and the Vlasov-Maxwell equation, to evaluate how the latencies impact real-world high-performance computing workloads. Our performance model shows that, while accounting for system overheads, a compact 1x256 bit single-wavelength photonic SRAM array, fabricated using the standard silicon photonics process by GlobalFoundries, sustains up to 1.5 TOPS, 0.9 TOPS, and 1.3 TOPS on the Sod shock tube problem, MTTKRP, and the Vlasov-Maxwell equation with an average energy efficiency of 2.5 TOPS/W.