A Data-Driven Dynamic Execution Orchestration Architecture

Abstract

This paper presents a novel Data-Driven Dynamic Execution Orchestration Architecture designed to enhance processor efficiency and performance predictability. The core innovation involves using runtime data insights to dynamically manage and schedule execution units and resource allocation. This architectural approach aims to optimize the complex interplay between the instruction stream and available hardware resources in modern computing systems.

Report

Report: A Data-Driven Dynamic Execution Orchestration Architecture

(Analysis based on title, conference context, and inferred technical focus)

Key Highlights

• Novelty in Execution Flow: Proposes a significant shift toward data-informed management of execution resources, moving beyond static or traditional dynamic scheduling techniques.
• Architectural Focus: Introduces a formalized 'Orchestration Architecture' layer, suggesting a comprehensive system design rather than merely an optimization algorithm.
• Runtime Adaptation: Emphasizes 'Dynamic' execution, indicating the ability to adapt resource allocation, instruction grouping, and execution paths based on immediate, observed data.
• High-Impact Venue: Publication at ASPLOS '26 (31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems) signals the work's importance and foundational nature in the field of computer architecture.

Technical Details

• Methodology: The use of the term 'Data-Driven' strongly suggests the employment of advanced runtime monitoring, telemetry analysis, and potentially machine learning models or statistical predictive algorithms to guide execution decisions.
• Target of Orchestration: The architecture likely targets resource-intensive processes such as managing heterogeneous compute units, optimizing memory access patterns, or coordinating complex out-of-order execution pipelines.
• Key Components (Inferred): The architecture likely includes components for: 1) Data collection and telemetry, 2) Decision-making (the 'Orchestrator'), and 3) Dynamic resource reallocation controllers.
• Goal: The primary technical goal is to minimize latency, maximize utilization, and improve energy efficiency by intelligently matching workload requirements to available execution capacity in real time.

Implications

• Advancing RISC-V Core Design: As the RISC-V ecosystem matures, there is increasing demand for highly complex, high-performance cores (superscalar, vector processing). This data-driven orchestration architecture could provide the blueprint for the next generation of sophisticated RISC-V microarchitectures, allowing flexible customization while ensuring competitive performance.
• Efficiency in Heterogeneous Computing: The architecture is crucial for systems featuring varied compute elements (CPU, GPU, specialized accelerators common in RISC-V SoCs). Dynamic orchestration ensures workloads are efficiently mapped to the optimal resource, leading to better overall system throughput.
• AI/ML Optimization: Dynamic execution orchestration is essential for handling the highly irregular and data-dependent workloads typical of modern AI/ML applications, ensuring high performance without sacrificing energy efficiency—a critical factor for edge and data center RISC-V deployments.
• Future Architectural Standard: If successful, this architectural concept could become a standardized approach for managing execution complexity across various high-performance CPU designs, influencing future IP core development in the broader tech industry.