Dynamic Neural Network Kernel Fusion for Compute Graph Optimization
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Solution Overview
Problem
Current techniques for kernel fusion in deep learning models are either impractical due to the need for extensive manual effort or limited by the inflexibility of pre-fused libraries and tensor compilers, which do not adequately optimize neural network performance across a broad range of computation scenarios.
Innovation Solution
A dynamic system that identifies and combines kernels based on their characteristics using a generated rule set, automatically generating instructions to optimize neural network architecture and resource allocation, thereby simplifying the compute graph and reducing memory access bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual kernel fusion is performed for critical networks, then execution performance is improved, but development time and programming resource allocation increase
Solution Approach 1:
The system performs self-optimization by automatically analyzing the computation graph and applying kernel fusion transformations without requiring manual programming. The optimization engine autonomously identifies fusion opportunities and generates optimized code, allowing the system to improve its own execution performance without external intervention.
Solution Approach 2:
The system dynamically changes optimization parameters based on the specific characteristics of each computation graph. It adjusts fusion strategies, memory allocation, and execution plans according to the actual data flow and computational patterns, rather than using fixed manual optimization rules.
2Ease of manufacture
If pre-fused kernel libraries are used, then ease of implementation is improved, but adaptability to different computation scenarios deteriorates
Solution Approach 1:
The system transitions from static pre-fused kernels to dynamic kernel generation. It adapts the computation graph optimization in real-time based on the specific scenario, data characteristics, and hardware resources available, allowing the same system to handle diverse computation scenarios effectively.
Solution Approach 2:
The optimization engine serves multiple functions: it analyzes the computation graph, identifies fusion opportunities, generates optimized kernels, and adapts to different hardware platforms. This universal system replaces the need for separate pre-fused libraries for each scenario while maintaining ease of implementation.
3Productivity
If tensor compilers with limited flexibility are used, then broad range performance is improved, but optimization flexibility deteriorates
Solution Approach 1:
The system segments the optimization process into distinct analytical phases: computation graph analysis, fusion opportunity identification, kernel generation, and execution planning. This segmentation allows each phase to be optimized independently while maintaining overall flexibility across different scenarios.
4Ease of operation
If kernels are processed sequentially without fusion, then implementation simplicity is improved, but memory access efficiency deteriorates
Solution Approach 1:
The system merges adjacent kernels in the computation graph into fused operations, combining multiple memory access patterns into a single optimized pass. This reduces the total number of memory fetches and stores while maintaining implementation simplicity through automatic code generation.
Data Source
AI summary
In various examples, systems and methods are disclosed herein for dynamically updating a neural network having a plurality of kernels. The system may identify a first subset of kernels from the plurality of kernels in the neural network. The system may then determine the characteristics of each respective kernel in the first subset. The system may then compare the characteristics of the respective kernels in the first subject to a dynamic rule set. In response to the system comparing the characteristics of the respective kernels in the first subset to the dynamic rule set, the system identifies a second subset of the first subset based on the comparing, automatically generates instructions to combine the second subset of kernels, and updates the neural network based on the one or more instructions. The neural network may have a simplified compute graph based on the above dynamic updating systems and methods.