Kernel Load Balancer for Neural Engine Inference Requests
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Solution Overview
Problem
It is challenging for application developers to efficiently utilize multiple circuit engines on computer chips, such as neural engines, for machine learning tasks due to complexity in load balancing and managing hardware resources.
Innovation Solution
A load balancer operating in the kernel space of the system manages the allocation of inference requests across neural engines, utilizing QoS-based weighting, historical performance data, and real-time hardware status to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a load balancer is implemented to manage multiple circuit engines, then resource utilization and throughput are improved, but system complexity increases
Solution Approach 1:
A kernel-space load balancer is introduced as an intermediary component between application developers and multiple circuit engines. The load balancer receives inference requests from user space, makes intelligent routing decisions based on engine availability and performance metrics, and distributes workloads appropriately. This mediator approach improves throughput by utilizing all available engines while managing complexity centrally in the kernel rather than requiring complex client-side logic.
Solution Approach 2:
The load balancer implements self-service mechanisms by automatically monitoring circuit engine status, detecting failures, and dynamically redistributing workloads without external intervention. The system autonomously tracks engine availability, adjusts load distribution in real-time, and handles failover scenarios, thereby improving productivity while keeping the management overhead contained within the kernel-space component.
2Ease of operation
If kernel-space load balancing is implemented, then ease of operation for developers is improved, but device complexity increases
Solution Approach 1:
The complexity of load balancing logic is extracted from user space and placed in kernel space. Application developers only need to submit inference requests through a simple interface without needing to understand or implement load balancing algorithms. The complex decision-making logic regarding engine selection, load distribution, and failure handling is extracted and encapsulated within the kernel-space load balancer, simplifying the developer experience while concentrating complexity where it can be effectively managed.
3Power
If multiple circuit engines are used, then processing capacity is improved, but difficulty of managing hardware resources increases
Solution Approach 1:
The load balancer is designed as a universal management layer that can handle multiple types of circuit engines (neural engines, NPU, GPU, FPGA) through a unified interface and common load balancing logic. This multi-functional approach allows the system to scale to different engine types and configurations without requiring separate management mechanisms for each, thereby increasing processing capacity while keeping hardware management complexity manageable through abstraction.
Data Source
AI summary
An electronic device may receive, at a first system routine from a client application, a provisioning request indicating that the application includes code for evaluating a machine learning model, wherein the first system routine executes in user space of memory on the device. The device may provision the code for execution on one or more of the circuit engines. The device may receive, at a second system routine from the application, an inference request for evaluating the machine learning model containing input data, wherein the second system routine executes in kernel space of memory on the device. The device may receive, at the second system routine, information about the circuit engines. The device may assign the inference request to one or more of the circuit engines where the request is evaluated. The device may provide a result of the inference request to the application.