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DTSTART;TZID=America/Los_Angeles:20250625T104500
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UID:dac_DAC 2025_sess114_RESEARCH1082@linklings.com
SUMMARY:SimPhony: A Device-Circuit-Architecture Cross-Layer Modeling and S
 imulation Framework for Heterogeneous Electronic-Photonic AI System
DESCRIPTION:Ziang Yin (Arizona State University); Meng Zhang, Amir Begovic
 , and Zhaoran Huang (Rensselaer Polytechnic Institute); and Jeff Zhang and
  Jiaqi Gu (Arizona State University)\n\nElectronic-photonic integrated cir
 cuits (EPICs) present a transformative solution for next-generation high-p
 erformance artificial intelligence (AI). The advancement of EPIC AI system
 s, however, requires extensive interdisciplinary research across devices, 
 circuits, architecture, and design automation. The complexity of hybrid sy
 stems makes it challenging even for domain experts to understand distinct 
 behaviors and interactions across design stacks. The lack of a flexible, a
 ccurate, fast, and easy-to-use EPIC AI system simulation framework signifi
 cantly hinders researchers from exploring their hardware innovations at di
 fferent sub-areas and evaluating the system impacts with common benchmarks
 . To address this gap, we propose SimPhony, a device-circuit-architecture 
 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 m
 ulti-core architectures with diverse photonic tensor core designs; (2) opt
 ics-specific dataflow modeling with unique multi-dimensional parallelism a
 nd reuse beyond spatial/temporal dimensions; (3) data-aware energy modelin
 g with realistic device responses, layout-aware area estimation, link budg
 et analysis, and bandwidth-adaptive memory modeling; and (4) seamless inte
 gration 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 ac
 ross multiple domains, facilitating the next leap in emerging AI hardware.
 \n\nTopics: AI\n\nTracks: AI4: AI/ML System and Platform Design\n\nSession
  Chairs: Xiaoxuan Yang (University of Virginia, Stanford University) and S
 hihao Song (Nvidia)\n\n
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