Over the past few years, several microring resonator (MRR)-based analog photonic architectures have been proposed to accelerate general matrix-matrix multiplications (GEMMs), which are found in abundance in deep learning workloads.These architectures have dramatically grown in popularity because they offer exceptional throughput and energy efficiency compared to their electronic counterparts. However, such architectures, due to their traditional realization based on the silicon-on-insulator (SOI) material platform, face two shortcomings. First, the high-index contrast of the SOI platform incurs high scattering losses, which mandates the provisioning of high optical input power.Second, SOI waveguides are susceptible to two-photon absorption, which can incur substantial optical signal losses at moderate-to-high signal fan-in. These shortcomings have severely detrimental effects on the achievable parallelism, throughput, and energy efficiency of SOI MRR-based GEMM accelerators. To address these shortcomings, we present a novel Silicon Nitride (SiN)-Based Photonic GEMM Accelerator called SiNPhAR. SiNPhAR architecture employs SiN-based active and passive devices to implement analog GEMM functions. Since the SiN material exhibits lower index contrast and no TPA, the optical signal losses in our SiNPhAR architecture are very low. This advantage significantly enhances the achievable processing parallelism, throughput, and energy efficiency of SiNPhAR architecture, compared to SOI-based photonic GEMM accelerators from prior work. We quantify and compare these benefits of SiNPhAR architecture via our cross-layer evaluation for a benchmark workload comprising four modern deep neural network models. From the system-level performance analysis, SiNPhAR demonstrates at least 1.7x better throughput FPS while consuming at least 2.8x better energy efficiency (FPS/W) than prior SOI-based GEMM accelerators.

翻译：近年来，基于微环谐振器的模拟光子架构被广泛提出，用于加速深度学习任务中大量出现的通用矩阵乘法（GEMM）。这类架构因其相较于电子方案具备卓越的吞吐量和能效而迅速普及。然而，由于传统上基于绝缘体上硅材料平台实现，此类架构面临两大缺陷：首先，SOI平台的高折射率对比导致高散射损耗，迫使系统提供高功率光输入；其次，SOI波导易产生双光子吸收效应，在中高信号扇入条件下会引发显著的光学信号损耗。这些缺陷严重制约了基于SOI微环谐振器的GEMM加速器在并行度、吞吐量和能效方面的性能。针对上述问题，我们提出一种新型氮化硅基光子GEMM加速器——SiNPhAR。该架构采用氮化硅有源/无源器件实现模拟GEMM功能。由于氮化硅材料具有更低折射率对比且无双光子吸收效应，SiNPhAR架构中的光学信号损耗极低。相较于先前基于SOI的光子GEMM加速器，该优势显著提升了SiNPhAR架构的处理并行度、吞吐量和能效。我们通过跨层级评估，以包含四个现代深度神经网络模型的基准工作负载为测试对象，量化并对比了SiNPhAR架构的这些优势。系统级性能分析表明，与先前的SOI基GEMM加速器相比，SiNPhAR的吞吐量（FPS）至少提升1.7倍，而能效（FPS/W）至少提升2.8倍。