MACHINE LEARNING-BASED PCIe BIFURCATION CONFIGURATION
개요
발명자
Yatzu HUANG
IPC 분류
CPC 분류
A dynamic method and system for configuring PCI Express (PCIe) bifurcation is provided. A baseboard management controller (BMC) receives system condition data representing current hardware configuration and operational metrics, including device presence, negotiated link widths, error counts, and thermal events. A trained machine learning model, such as a decision tree, predicts boot success outcomes for multiple candidate bifurcation configurations. The BMC selects a preferred configuration based on the predictions and writes it to a reserved memory buffer (RMB). During subsequent initialization, a basic input/output system (BIOS) retrieves the preferred configuration from the RMB and applies it to initialize PCIe links. The model is periodically retrained with new boot outcomes to refine predictions, enabling adaptive, self-learning bifurcation without repeated BIOS recompilation or reflashing.
원문 (중국어)
A dynamic method and system for configuring PCI Express (PCIe) bifurcation is provided. A baseboard management controller (BMC) receives system condition data representing current hardware configuration and operational metrics, including device presence, negotiated link widths, error counts, and thermal events. A trained machine learning model, such as a decision tree, predicts boot success outcomes for multiple candidate bifurcation configurations. The BMC selects a preferred configuration based on the predictions and writes it to a reserved memory buffer (RMB). During subsequent initialization, a basic input/output system (BIOS) retrieves the preferred configuration from the RMB and applies it to initialize PCIe links. The model is periodically retrained with new boot outcomes to refine predictions, enabling adaptive, self-learning bifurcation without repeated BIOS recompilation or reflashing.