PISum 统一光子推理模拟器发布，支持硬件感知跨层设计空间探索
PISim: A Unified Photonic Inference Simulator for Hardware-Aware Cross-Layer Design-Space Exploration
AI 导读
PISim 是一个统一的光子推理模拟器，将预训练深度神经网络执行与 6 类加速器架构、6 种电光调制器技术、4 种光源组织的物理模型耦合。在 MNIST MLP 基准上，Mach-Zehnder 与微环调制器在多损伤压力下最佳精度为 6 bits，VCSEL 阵列与并行扇出激光架构之间存在 52.5 个百分点的精度差距，并模拟了 32 到 128 通道的接口受限吞吐量扩展。
正文 · 原文
Abstract:Photonic neural accelerators offer ultra-low latency linear algebra, yet their usable numerical precision, sustainable throughput, and application accuracy remain tightly bounded by surrounding electro-optical interfaces and physical non-idealities. Pre-fabrication design-space exploration (DSE) requires a cross-layer electronic design automation (EDA) framework capable of evaluating disparate optical topologies under identical neural workloads and physical assumptions. We present \textbf{PISim}, a unified Photonic Inference Simulator that couples pretrained deep neural network execution with physics-based models of six accelerator architecture families, six electro-optic modulator technologies, four optical source organizations, and direct or coherent readout schemes. PISim embeds stage-resolved impairment operators---covering control and analog-to-digital quantization, finite extinction ratio, shot noise, correlated relative-intensity noise (RIN), thermal drift, propagation loss, and channel crosstalk---alongside an analytical cycle-level interface timing model to expose electronic-optical bottlenecks. Using simulated MNIST multilayer perceptron (MLP) benchmarks, we demonstrate how PISim identifies optimal precision operating points under multi-impairment stress (peaking at 6 bits for Mach--Zehnder and microring modulators), quantifies a 52.5 percentage-point accuracy gap between VCSEL-array and parallel-fanout laser architectures arising, under the modeled conditions, from power partitioning and correlated source noise, and models interface-limited throughput scaling across emitter-array widths from 32 to 128 channels. This comprehensive framework establishes an extensible, reproducible foundation for accuracy-constrained Pareto optimization prior to photonic tape-out.
| Comments: | 8 pages, 2 figures |
| Subjects: | Optics (physics.optics); Artificial Intelligence (cs.AI) |
| MSC classes: | I.6.7 |
| Cite as: | arXiv:2611.16522 [physics.optics] |
| (or arXiv:2611.16522v1 [physics.optics] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16522 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Andreas Falkenberg [view email]
[v1]
Thu, 12 Nov 2026 09:18:27 UTC (34 KB)
来源：arXiv cs.AI · arxiv.org