Neural Network Instruction Sequencing via Graph Partitioning
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Solution Overview
Problem
Existing neural network model compilation methods struggle to generate optimal instruction sequences due to the lack of hardware-specific optimizations, leading to suboptimal performance and increased search costs.
Innovation Solution
The method involves partitioning a computation graph into sub-graphs, generating instruction sub-sequences based on resource status information, and determining a target instruction sequence using reinforcement learning to optimize resource allocation and reduce search costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the computation graph is processed as a whole without partitioning, then the instruction sequence generation maintains simplicity, but the performance optimization and resource allocation efficiency deteriorate
Solution Approach 1:
The computation graph is partitioned into multiple computation sub-graphs based on hardware resource constraints and data dependencies. Each sub-graph can be independently optimized and mapped to specific hardware resources, enabling parallel processing and improving overall instruction sequence generation efficiency while maintaining manageable complexity through modular processing
2Reliability
If hardware-specific optimizations are applied to generate optimal instruction sequences, then the execution performance improves, but the search cost and compilation time increase
Solution Approach 1:
Resource status information is obtained in advance before instruction sequence generation. The system pre-analyzes hardware resource availability, capabilities, and constraints, storing this information for use during the compilation process. This preliminary action enables faster optimization decisions during instruction sequence generation without requiring extensive search during compilation
Solution Approach 2:
Different computation sub-graphs are optimized based on local hardware resource status and specific computational requirements. Each sub-graph receives tailored optimization strategies according to the available hardware resources and data dependencies, rather than applying uniform optimization across the entire computation graph, thus improving performance while reducing overall search complexity
Data Source
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AI summary
Disclosed are a method and apparatus for generating an instruction sequence, an electronic device, and a storage medium. The method for generating an instruction sequence includes: determining a computation graph corresponding to a neural network model to be compiled and resource status information on hardware executing the instruction sequence; partitioning the computation graph, to determine multiple computation sub-graphs; generating, based on the computation sub-graphs and the resource status information, instruction sub-sequences corresponding respectively to the computation sub-graphs; and determining, based on the instruction sub-sequences, a target instruction sequence corresponding to the neural network model to be compiled.