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

• URL: http://arxiv.org/abs/2411.13715v1
• Date: Wed, 20 Nov 2024 21:21:54 GMT
• Title: SimPhony: A Device-Circuit-Architecture Cross-Layer Modeling and Simulation Framework for Heterogeneous Electronic-Photonic AI System
• Authors: Ziang Yin, Meng Zhang, Amir Begovic, Rena Huang, Jeff Zhang, Jiaqi Gu,
• Abstract summary: We propose SimPhony, a cross-layer modeling and simulation framework for heterogeneous electronic-photonic AI systems. By providing a unified, versatile, and high-fidelity simulation platform, SimPhony enables researchers to innovate and evaluate AI hardware across multiple domains.
• Score: 7.378742476019604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• An Efficient and Mixed Heterogeneous Model for Image Restoration [71.85124734060665]
  Current mainstream approaches are based on three architectural paradigms: CNNs, Transformers, and Mambas. We propose RestorMixer, an efficient and general-purpose IR model based on mixed-architecture fusion.
  arXiv  Detail & Related papers  (2025-04-15T08:19:12Z)
• Understanding and Optimizing Multi-Stage AI Inference Pipelines [11.254219071373319]
  HERMES is a Heterogeneous Multi-stage LLM inference Execution Simulator. HERMES supports heterogeneous clients executing multiple models concurrently unlike prior frameworks. We explore the impact of reasoning stages on end-to-end latency, optimal strategies for hybrid pipelines, and the architectural implications of remote KV cache retrieval.
  arXiv  Detail & Related papers  (2025-04-14T00:29:49Z)
• MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure [18.220589086200025]
  Inverse design has emerged as a transformative approach for photonic device optimization. We introduce MAPS, a multi-fidelity AI-augmented photonic simulation and inverse design infrastructure.
  arXiv  Detail & Related papers  (2025-03-02T22:30:18Z)
• PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices [14.671301859745453]
  Existing SOTA approaches, NeurOLight, struggle with predicting high-fidelity fields for real-world complicated photonic devices. We propose a novel cross-axis factorized PACE operator with a strong long-distance modeling capacity. Inspired by human learning, we conquer the simulation task for extremely hard cases into two progressively easy tasks.
  arXiv  Detail & Related papers  (2024-11-05T22:03:14Z)
• A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
  ARCANA is a spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits. We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software.
  arXiv  Detail & Related papers  (2024-09-23T11:16:46Z)
• Flemme: A Flexible and Modular Learning Platform for Medical Images [5.086862917025204]
  Flemme is a FLExible and Modular learning platform for MEdical images. We construct encoders using building blocks based on convolution, transformer, and state-space model (SSM) to process both 2D and 3D image patches.
  arXiv  Detail & Related papers  (2024-08-18T05:47:33Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators [77.89070422157178]
  Manufacturers of AI-integrated products face a critical challenge: selecting an accelerator that aligns with their product's performance requirements. The Abstract Computer Architecture Description Language (ACADL) is a concise formalization of computer architecture block diagrams. In this paper, we demonstrate how to use the ACADL to model AI hardware accelerators, use their ACADL description to map DNNs onto them, and explain the timing simulation semantics to gather performance results.
  arXiv  Detail & Related papers  (2024-01-30T19:27:16Z)
• DEAP: Design Space Exploration for DNN Accelerator Parallelism [0.0]
  Large Language Models (LLMs) are becoming increasingly complex and powerful to train and serve. This paper showcases how hardware and software co-design can come together and allow us to create customized hardware systems.
  arXiv  Detail & Related papers  (2023-12-24T02:43:01Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• AI-Empowered Hybrid MIMO Beamforming [85.48860461696417]
  Hybrid multiple-input multiple-output (MIMO) systems implement part of their beamforming in analog and part in digital. Recent years have witnessed a growing interest in using data-aided artificial intelligence (AI) tools for hybrid beamforming design. This article reviews candidate strategies to leverage data to improve real-time hybrid beamforming design.
  arXiv  Detail & Related papers  (2023-03-03T06:04:20Z)
• A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models [6.072718806755325]
  We develop an original brain-inspired neural model associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant SOM (ReSOM) model. This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform.
  arXiv  Detail & Related papers  (2022-01-06T22:02:19Z)
• NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework [5.509715131727269]
  We present SimNet, an AI-driven multi-physics simulation framework, to accelerate simulations across a wide range of disciplines. SimNet addresses a wide range of use cases - coupled forward simulations without any training data, inverse and data assimilation problems.
  arXiv  Detail & Related papers  (2020-12-14T20:55:48Z)
• RoboTHOR: An Open Simulation-to-Real Embodied AI Platform [56.50243383294621]
  We introduce RoboTHOR to democratize research in interactive and embodied visual AI. We show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs.
  arXiv  Detail & Related papers  (2020-04-14T20:52:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.