Deep Learning Algorithm Compilation Adaptation
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Solution Overview
Problem
Deep learning algorithms and processors face challenges in performance optimization due to extreme sensitivity to changes in algorithms and hardware, coupled with high coupling between operations and data, making ahead-of-time compilation inefficient.
Innovation Solution
A deep learning algorithm compiling method that receives operation data, determines instruction types, and executes corresponding compilation operations to generate binary code, utilizing a neurological calculus library API to adaptively optimize compilation and improve hardware platform performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If ahead-of-time compilation is used for deep learning algorithms, then compilation time is reduced, but performance optimization effectiveness deteriorates due to extreme sensitivity to algorithm and hardware changes
Solution Approach 1:
The patent implements a dynamic compilation approach where the compilation process adapts to different algorithm and hardware configurations. The system determines instruction types based on operation data and executes corresponding compilation operations, allowing the compilation strategy to change dynamically rather than using a static ahead-of-time compilation approach. This resolves the contradiction by making compilation time-efficient while maintaining performance optimization effectiveness through adaptability.
Solution Approach 2:
The system changes compilation parameters based on the determined instruction types and operation characteristics. By adjusting compilation strategies according to specific operation types and hardware platforms, the system achieves both efficient compilation and effective performance optimization, resolving the trade-off between compilation speed and optimization quality.
2Adaptability or versatility
If compilation process is made adaptive to different instruction types, then compilation flexibility improves, but compilation process complexity increases
Solution Approach 1:
The patent segments the compilation process into distinct stages: receiving operation data, determining instruction types, and executing corresponding compilation operations. This segmentation allows the system to handle different instruction types independently through standardized procedures, improving flexibility while managing complexity through modular design rather than monolithic complex processing.
Solution Approach 2:
The system introduces an intermediary instruction type determination step that acts as a mediator between the raw operation data and the compilation execution. This intermediary layer classifies and routes different operation types to appropriate compilation routines, enabling adaptability without requiring the entire compilation process to be complex and interconnected.
3Manufacturing precision
If high coupling between operations and data is maintained, then algorithm accuracy is preserved, but compilation efficiency deteriorates
Solution Approach 1:
The patent dynamically adjusts the coupling between operations and data during compilation based on instruction types. For operations where high coupling is essential for accuracy, the system maintains tight integration. For other operations, it decouples processing to improve compilation efficiency. This dynamic approach resolves the contradiction by adapting the coupling level to the specific requirements of each operation type.
Data Source
AI summary
The present disclosure relates to a deep learning algorithm compiling method and a device and a related product, the product comprising a controller unit, and the controller unit comprising: an instruction cache unit, an instruction processing unit, and a queue-storing unit. The instruction cache unit is configured to store computation instructions associated with artificial neural network operations. The instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions. The queue-storing unit is configured to store an instruction queue, which comprises: a plurality of operation instructions or computation instructions to be executed according to the front-to-rear sequence of the queue. By means of the described method, the present disclosure may improve the operation efficiency of the related product when carrying out neural network model operations.