Bridging the Gap: Physical PCI Device Integration Into SystemC-TLM Virtual Platforms

2025-05-21

AI Summary

To address the challenges of complex PCI device modeling and low simulation performance in SystemC-TLM virtual platforms—hindering efficient early-stage execution and debugging of AI software—this paper proposes a hardware-level native PCI device embedding mechanism. It achieves, for the first time, seamless integration of physical PCIe devices (e.g., Google Coral Edge TPU) into SystemC-TLM-2.0 platforms. Leveraging a lightweight PCIe protocol bridge and hardware-software co-simulation, the approach bypasses conventional virtual device modeling, enabling unmodified AI applications to run natively. Evaluated on an ARM-based virtual platform, it delivers a 480× speedup in AI workload simulation while preserving driver development compatibility and enabling cross-architecture regression testing. This significantly enhances the practicality and productivity of virtual platforms in AI hardware-software co-design.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Interaction Techniques and Devices

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In today's technology-driven world, early-stage software development and testing are crucial. Virtual Platforms (VPs) have become indispensable tools for this purpose as they serve as a platform to execute and debug the unmodified target software at an early design stage. With the increasing complexity of software, especially in areas like Artificial Intelligence (AI) applications, VPs need to provide high simulation speed to ensure the target software executes within a reasonable time. Hybrid simulation, which combines virtual models with real hardware, can improve the performance of VPs. This paper introduces a novel approach for integrating real Peripheral Component Interconnect (PCI) devices into SystemC-TLM-2.0-based VPs. The embedded PCI devices enable high performance, easy integration, and allow introspection for analysis and optimization. To illustrate the practical application of our approach, we present a case study where we integrate Google Coral's Edge Tensor Processing Unit (TPU) into an ARM-based VP. The integration allows efficient execution of AI workloads, accelerating simulation speeds by up to 480x while eliminating the need for complex virtual device models. Beyond accelerating AI-workload execution, our framework enables driver development, regression testing across architectures, and device communication analysis. Our findings demonstrate that embedding PCI devices into SystemC simulations significantly enhances

Problem

• Integrating real PCI devices into SystemC-TLM virtual platforms
• Accelerating AI workload execution in virtual simulations
• Enabling driver development and cross-architecture regression testing

Innovation

• Integrates real PCI devices into SystemC-TLM-2.0 VPs
• Enables high performance and easy device integration
• Accelerates AI workloads by 480x via PCI-TPU embedding