Domain Controller Resource Allocation for Multi-Processor Latency
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Solution Overview
Problem
The challenge of efficiently allocating resources such as computing power, memory, and bandwidth among multiple processors in a domain controller, leading to inefficiencies and resource wastage, particularly in complex in-vehicle systems like intelligent driving and cockpit control, is not adequately addressed by conventional manual allocation methods.
Innovation Solution
A resource allocation method that simulates service running in various scenarios to optimize resource configuration strategies and neural network configurations across multiple processors, allowing for adaptive and automated deployment from an overall perspective, thereby improving efficiency and reducing communication latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual resource allocation is used among multiple processors, then resource allocation can be performed, but resource utilization is low and resource wastage occurs
Solution Approach 1:
The system performs self-service through automated resource allocation. The resource allocation module automatically determines optimal resource distribution among multiple processors based on service requirements and system state, eliminating manual allocation inefficiencies. The system monitors its own resource usage and dynamically adjusts allocation without external intervention, achieving both high productivity and reduced resource wastage.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring resource allocation results and service performance metrics. The resource allocation module receives feedback about resource utilization efficiency and service quality, then adjusts resource distribution accordingly. This closed-loop control ensures optimal resource utilization while preventing wastage, resolving the contradiction between allocation efficiency and resource loss.
2Productivity
If multiple processors are deployed to meet computing requirements, then processing capability is improved, but resource allocation complexity increases
Solution Approach 1:
The resource allocation module serves multiple functions within a single integrated component. It performs service matching, resource distribution, performance monitoring, and dynamic adjustment across all processors. This universal module handles diverse allocation scenarios (computing power, memory, bandwidth) through a unified approach, maintaining high processing capability while managing complexity centrally rather than through multiple specialized systems.
Solution Approach 2:
The system manages complexity by dynamically changing allocation parameters based on service requirements and system state. Rather than fixing complex allocation rules, the module adjusts parameters such as resource distribution ratios, priority levels, and allocation thresholds in response to varying conditions. This parameter-driven approach enables flexible resource management across multiple processors without requiring complex hard-coded allocation logic.
3Ease of operation
If manual resource allocation is used, then implementation is simple, but adaptation to service requirements is poor
Solution Approach 1:
The resource allocation module transitions from static manual allocation to dynamic automated allocation. It continuously adapts resource distribution based on real-time service requirements, processor performance, and system state changes. The module dynamically adjusts allocation decisions without requiring complex manual reconfiguration, maintaining ease of operation while achieving superior service adaptation through automated responsiveness to changing conditions.
Data Source
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AI summary
This application discloses a resource allocation method, an apparatus, a system, and a transportation means, and relates to the field of processor technologies. In this application, a topology relationship between a plurality of service applications for which a plurality of processors in a controller are responsible, a communication protocol that the plurality of service applications comply with, and a deployment strategy are determined through deployment simulation. Then, a resource allocation status and a neural network configuration status of the plurality of processors in the controller are determined through running simulation. Then, a resource configuration strategy and/or a neural network configuration of the controller are/is adjusted through deployment optimization, to obtain a better resource allocation solution. Finally, resources of the plurality of processors in the controller are allocated based on a deployment optimization result to the plurality of service applications corresponding to the plurality of processors. Based on this, resource allocation can be automatically implemented from an overall perspective of the controller, to improve efficiency, properness, and scientificity of resource allocation or adjustment, and ensure a low communication latency and low power consumption in a process in which a domain controller performs a corresponding service.