Dynamic Data Representation Management for Heterogeneous Deep Learning Resources
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Solution Overview
Problem
Deep learning systems driven by heterogeneous resources face inefficiencies in data representation management, leading to suboptimal performance and energy consumption due to inadequate handling of data formats across different resources.
Innovation Solution
A method is introduced to dynamically manage data representation in deep learning systems by setting optimized data formats for each resource based on path information and data representation information, allowing for efficient data sharing without memory copies and reducing communication costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is processed using fixed data representation formats across heterogeneous resources, then system simplicity is maintained, but performance and energy efficiency deteriorate
Solution Approach 1:
The patent implements dynamic data representation management where the system automatically selects and configures optimal data formats (such as precision levels, data types) based on the specific computing path and resource characteristics. This allows the data representation to adapt dynamically rather than remaining fixed, resolving the contradiction between maintaining simplicity and achieving high performance.
Solution Approach 2:
The system changes key parameters of data representation (precision, format, type) according to the computing path and resource capabilities. By adjusting these parameters dynamically, the system achieves optimal performance on heterogeneous resources without requiring complex manual configuration, thus improving productivity while managing complexity through automation.
2Productivity
If data format conversion is performed between heterogeneous resources, then performance is improved, but communication overhead and energy consumption increase
Solution Approach 1:
The patent performs data representation configuration in advance based on the computed optimal computing path. By pre-configuring the data formats and representations before execution, the system avoids unnecessary real-time conversions and reduces communication overhead, thereby improving performance while minimizing energy consumption.
Solution Approach 2:
The system introduces an intermediary layer (the data representation management module) that mediates between heterogeneous resources. This intermediary automatically handles format conversions and optimizations, allowing resources to communicate efficiently without direct overhead, thus improving performance while controlling energy usage through intelligent intermediation.
3Loss of energy
If optimized data representation is applied to each heterogeneous resource, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically determines and configures optimal data representations for each resource without requiring manual intervention. The automated path computation and format selection processes enable the system to serve itself, achieving energy efficiency through optimized representations while avoiding the complexity burden on users.
Solution Approach 2:
The data representation management system serves multiple functions: it computes optimal paths, selects appropriate data formats, configures resources, and monitors performance. This multi-functional universal approach consolidates complexity into a single management layer that handles all optimization tasks, improving energy efficiency while presenting a unified interface that masks the underlying complexity.
Data Source
AI summary
A method of processing data for a deep learning system driven by a plurality of heterogeneous resources is provided. The method includes, when a first task including at least one of a plurality of operations is to be performed, receiving first path information indicating a first computing path for the first task. The first computing path includes a sequence of operations included in the first task and a driving sequence of resources for performing the operations included in the first task. The method further includes setting data representation formats of the resources for performing the operations included in the first task based on data representation information and the first path information. The data representation information indicates an optimized data representation format for each of the plurality of heterogeneous resources.