Neural Network Compilation Toolchain With IR and Hardware-Aware Optimization
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Solution Overview
Problem
Current neural network compilation and optimization tools suffer from high encapsulation, limited user interfaces, lack of transparency, rigid optimization methods, and poor hardware portability, preventing effective fine-tuning and deployment across diverse platforms.
Innovation Solution
A method and system for compiling neural networks that involve translating network files into intermediate representations, optimizing them through performance analysis, single-node, and collaborated optimization, and generating executable inference applications, utilizing an automated compilation toolchain framework that adjusts parameters and applies optimization algorithms based on software and hardware information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compilation tools apply predefined optimization rules directly, then optimization speed is improved, but optimization effectiveness deteriorates due to rigid methods missing front-end opportunities and lacking portability
Solution Approach 1:
The patent segments the compilation process into distinct phases: front-end optimization (operator fusion, common subexpression replacement) and back-end optimization (hardware-specific techniques like loop unrolling and vectorization). This segmentation allows each phase to be optimized independently with appropriate strategies, improving both speed and effectiveness without requiring substantial human expert intervention
Solution Approach 2:
The patent introduces configurable parameters and rules that can be adjusted based on target hardware characteristics and performance requirements. By changing optimization parameters dynamically rather than using fixed predefined rules, the system achieves better portability across diverse hardware platforms while maintaining optimization effectiveness
2Device complexity
If traditional tools use high encapsulation architecture, then system complexity is reduced, but user control and fine-tuning capability deteriorate due to limited interfaces and lack of transparency
Solution Approach 1:
The patent introduces an intermediate representation layer that serves as a mediator between the high-level network definition and low-level hardware implementation. This intermediate layer provides transparency into the optimization process while maintaining manageable system complexity, allowing users to inspect and control optimization steps without directly managing low-level complexity
Solution Approach 2:
The patent implements a dynamic optimization framework where optimization rules and parameters can be adjusted based on user input, hardware characteristics, and performance feedback. This dynamic approach enables fine-tuning capability while keeping the overall system architecture manageable through automated adaptation
3Productivity
If back-end optimizations are hardware-specific, then performance on target hardware is improved, but portability across diverse hardware platforms deteriorates
Solution Approach 1:
The patent creates a universal intermediate representation that can represent neural network operations independently of specific hardware. This universal representation layer enables the same optimized code to be deployed across diverse hardware platforms while allowing hardware-specific optimizations to be applied uniformly, achieving both high performance and broad portability
Data Source
AI summary
A method and a system for compiling a neural network, a computer storage medium, and a compilation device are provided. The method for compiling the neural network comprises: translating a network file into an intermediate representation file; optimizing the intermediate representation file to obtain an optimized intermediate representation file, based on a performance analysis, single-node optimization, and collaborated optimization; generating a network template file based on hardware interfaces through the optimized intermediate representation file; compiling the network template file into an executable inference application. The present disclosure aims to design and implement an automated compilation toolchain framework. This framework adjusts parameters, generates code, creates intermediate representations (IRs), and applies optimization algorithms based on software and hardware information. When this compilation toolchain operates on a target chip, it ensures consistent network output results, achieves higher computation rates within shorter optimization times, reduces computation delays, and facilitates user debugging and tuning.