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.

翻译：光子内存计算是一种高速、低能耗的替代方案，旨在取代传统的基于晶体管的数字计算，其利用了光子操作的高频率和宽带宽。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，该模型捕捉了关键延迟源（如外部内存访问和光电转换）的影响。我们在一系列工作负载上执行了从算法到硬件的映射，包括Sod激波管问题、矩阵化张量乘Khatri-Rao积（MTTKRP）以及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。我们的性能模型表明，在考虑系统开销的情况下，一个采用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激波管问题、MTTKRP和Vlasov-Maxwell方程上分别可持续实现高达1.5 TOPS、0.9 TOPS和1.3 TOPS的算力，平均能效为2.5 TOPS/W。