mlsanityData to 14 Oct 2026

SimPhony: A Device-Circuit-Architecture Cross-Layer Modeling and Simulation Framework for Heterogeneous Electronic-Photonic AI System

arXiv 2411.13715 ↗First posted 20 Nov 2024physics.optics cs.AI cs.ARcited by 7

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Abstract (arXiv)

Electronic-photonic integrated circuits (EPICs) offer transformative potential for next-generation high-performance AI but require interdisciplinary advances across devices, circuits, architecture, and design automation. The complexity of hybrid systems makes it challenging even for domain experts to understand distinct behaviors and interactions across design stack. The lack of a flexible, accurate, fast, and easy-to-use EPIC AI system simulation framework significantly limits the exploration of hardware innovations and system evaluations on common benchmarks. To address this gap, we propose SimPhony, a cross-layer modeling and simulation framework for heterogeneous electronic-photonic AI systems. SimPhony offers a platform that enables (1) generic, extensible hardware topology representation that supports heterogeneous multi-core architectures with diverse photonic tensor core designs; (2) optics-specific dataflow modeling with unique multi-dimensional parallelism and reuse beyond spatial/temporal dimensions; (3) data-aware energy modeling with realistic device responses, layout-aware area estimation, link budget analysis, and bandwidth-adaptive memory modeling; and (4) seamless integration with model training framework for hardware/software co-simulation. By providing a unified, versatile, and high-fidelity simulation platform, SimPhony enables researchers to innovate and evaluate EPIC AI hardware across multiple domains, facilitating the next leap in emerging AI hardware. We open-source our codes at https://github.com/ScopeX-ASU/SimPhony

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 13 of its 37 references that are in the map. First posted on arXiv 20 Nov 2024.

Builds on

13 of 37 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Photonics for artificial intelligence and neuromorphic computingcited by 112
  3. 2016Neuromorphic Silicon Photonic Networkscited by 68
  4. 2021L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimizationcited by 21
  5. 2020Photonic tensor cores for machine learningcited by 16
  6. 2021A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learningcited by 13
  7. 2021O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speedcited by 6
  8. 2024TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulatorcited by 6
  9. 2024SCATTER: Algorithm-Circuit Co-Sparse Photonic Accelerator with Thermal-Tolerant, Power-Efficient In-situ Light Redistributioncited by 6
  10. 2023M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interferencecited by 5
  11. 2024Architecture-Level Modeling of Photonic Deep Neural Network Acceleratorscited by 4
  12. 2021Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesiscited by 3
1 more
  1. 2023Mirage: An RNS-Based Photonic Accelerator for DNN Trainingcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.