Dynamic Operator Implementation for Deep Learning Efficiency
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Solution Overview
Problem
Existing deep learning frameworks lack a low-cost operator implementation mode that can improve hardware performance, leading to inefficiencies in data processing and increased costs.
Innovation Solution
The method involves obtaining attribute information of input data and computing devices, selecting an optimal operator implementation mode based on this information, and generating operators by combining sub-operators to improve hardware performance and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional operator implementation modes are used in deep learning frameworks, then hardware performance is maintained at baseline levels, but computing efficiency is reduced and costs increase
Solution Approach 1:
The patent implements dynamic operator implementation by automatically selecting different execution strategies based on runtime conditions. The system evaluates multiple candidate implementation modes and dynamically chooses the optimal one for each operator, allowing the system to adapt to varying hardware states and data characteristics, thereby improving computing efficiency without manual intervention.
Solution Approach 2:
The patent changes implementation parameters by adjusting operator execution strategies based on input data characteristics and hardware conditions. Different operators are implemented with varying degrees of parallelization, memory allocation, and computation methods, optimizing performance for specific scenarios while maintaining flexibility across diverse workloads.
2Productivity
If operator implementation modes are manually optimized, then hardware performance can be improved, but the workload of operator development increases significantly
Solution Approach 1:
The patent implements self-service optimization where the system automatically analyzes operator characteristics and selects optimal implementation modes without human intervention. The automated selection mechanism evaluates multiple candidates and chooses the best implementation based on predefined criteria, eliminating the need for manual optimization work while maintaining high hardware performance.
Solution Approach 2:
The patent performs preliminary analysis of operator implementation options during the framework setup phase. Multiple candidate implementation modes are pre-evaluated and stored, allowing the system to quickly select the optimal one during runtime without requiring real-time manual optimization, thus reducing development workload while preserving performance benefits.
3Adaptability or versatility
If multiple operator implementation modes are supported, then adaptability to different hardware is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal operator implementation framework that supports multiple execution strategies through a unified interface. The system maintains a library of candidate implementation modes that can be selectively applied based on hardware characteristics, providing broad hardware adaptability while presenting a consistent abstraction layer to users and reducing perceived complexity.
Data Source
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AI summary
The disclosure provides a method for processing data, and an electronic device. The method includes: obtaining first attribute information of input data and second attribute information of a computing device corresponding to the input data; selecting a target operator implementation mode from a plurality of candidate operator implementation modes based on the first attribute information and the second attribute information; determining a plurality of sub-operators included in an operator required for the input data from an operator library based on the target operator implementation mode, to generate the operator; and obtaining an operation result by performing an operation on the input data by the computing device based on the operator.