Neural Task Manager Dependency Checking for Low-Latency Scheduling
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Solution Overview
Problem
Existing neural processing units face challenges in efficiently managing and checking task dependencies, leading to increased latency and reduced efficiency in deep-learning tasks due to the need for extensive synchronization and task scheduling by the command processor.
Innovation Solution
A task manager within the neural processing device that includes a task buffer, multiple queues, and a dependency checker to autonomously manage and check task dependencies, minimizing traffic with the command processor and optimizing task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the command processor manages and checks task dependencies, then task scheduling is centralized and controlled, but processing latency increases and efficiency decreases
Solution Approach 1:
The dependency checking function is extracted from the command processor and transferred to the task manager. The task manager now autonomously checks task dependencies using a dependency matrix, while the command processor only performs high-level task scheduling. This extraction eliminates the bottleneck caused by centralized dependency checking and reduces processing latency.
Solution Approach 2:
The task management system is segmented into distinct functional modules: the command processor handles task submission and high-level scheduling, while the task manager handles task distribution, execution monitoring, and dependency checking. This segmentation allows parallel processing of task scheduling and dependency validation, improving overall efficiency.
2Reliability
If extensive synchronization is performed for task dependencies, then task execution accuracy is maintained, but processing speed decreases
Solution Approach 1:
The dependency matrix is pre-computed and stored in the task manager, containing all dependency relationships between tasks. When a task is submitted, the task manager quickly checks the pre-computed matrix to determine dependencies, rather than performing extensive real-time synchronization analysis. This preliminary action maintains execution accuracy while dramatically improving processing speed.
3Productivity
If the task manager autonomously checks dependencies, then traffic with command processor is minimized, but device complexity increases
Solution Approach 1:
The task manager is designed as a multi-functional unit that combines task reception, task distribution, execution monitoring, and dependency checking capabilities. By consolidating these functions in a single autonomous module, the system reduces communication overhead with the command processor while the internal complexity is managed through integrated design.
4Productivity
If parallel task execution is enabled, then computation efficiency is enhanced, but dependency management complexity increases
Solution Approach 1:
The dependency relationships are copied into a compact matrix representation stored in the task manager. This matrix copy allows rapid parallel checking of dependencies without requiring complex real-time coordination between tasks. The copied dependency information enables efficient parallel execution while maintaining accurate dependency tracking.
Data Source
AI summary
A task manager, a neural processing device, and a method for checking task dependencies thereof are provided. The task manager includes a task buffer configured to receive first and second tasks of different first and second types, a first queue configured to receive a first task descriptor for the first task from the task buffer, a second queue configured to receive a second task descriptor for the second task from the task buffer, a dependency checker configured to check dependencies of the first and second task descriptors, a third queue configured to receive the first task descriptor from the dependency checker, and a fourth queue configured to receive the second task descriptor from the dependency checker.