AEG：一种通过直接硬件访问在异构加速器上实现AI加速的裸金属框架
AEG: A Baremetal Framework for AI Acceleration via Direct Hardware Access in Heterogeneous Accelerators
AI总结：
本文提出一种统一的裸金属运行时架构，用于在异构加速器上实现高性能机器学习推理，无需底层实时或通用操作系统。通过将复杂控制逻辑扁平化为可执行的Runtime Control Blocks，实现高阶模型的高效执行。
摘要：
本文介绍了一种统一的、硬件无关的裸金属运行时架构，旨在通过直接硬件访问在异构加速器（如AI引擎数组）上实现高性能机器学习（ML）推理，而无需底层实时或通用操作系统。现有边缘部署框架如TinyML通常依赖实时操作系统（RTOS），引入了不必要的复杂性和性能瓶颈。为此，我们的解决方案通过将运行时与硬件细节解耦，将复杂控制逻辑扁平化为线性可执行的Runtime Control Blocks（RCBs）。这种
英文摘要：
This paper introduces a unified, hardware-independent baremetal runtime architecture designed to enable high-performance machine learning (ML) inference on heterogeneous accelerators, such as AI Engine (AIE) arrays, without the overhead of an underlying real-time or general-purpose operating system. Existing edge-deployment frameworks, such as TinyML, often rely on real-time operating systems (RTOS), which introduce unnecessary complexity and performance bottlenecks. To address this, our solution fundamentally decouples the runtime from hardware specifics by flattening complex control logic into linear, executable Runtime Control Blocks (RCBs). This "Control as Data" paradigm allows high-level models, including Adaptive Data Flow (ADF) graphs, to be executed by a generic engine through a minimal Runtime Hardware Abstraction Layer (RHAL). We further integrate Runtime Platform Management (RTPM) to handle system-level orchestration (including a lightweight network stack) and a Runtime In-Memory File System (RIMFS) to manage data in OS-free environments. We demonstrate the framework's efficacy with a ResNet-18 image classification implementation. Experimental results show 9.2$\times$ higher compute efficiency (throughput per AIE tile) compared to Linux-based Vitis AI deployment, 3--7$\times$ reduction in data movement overhead, and near-zero latency variance (CV~$=0.03\%$). The system achieves 68.78\% Top-1 accuracy on ImageNet using only 28 AIE tiles compared to Vitis AI's 304 tiles, validating both the efficiency and correctness of this unified bare-metal architecture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
