Neural Network Metadata Tagging for Load Balancing
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Solution Overview
Problem
Deep neural networks (DNNs) face significant computational challenges due to their serial nature, leading to performance issues and inefficiencies in training, especially with deeper networks, as existing layer-fusion techniques are complex, error-prone, and not intuitive, and do not leverage dynamic edits like pruning effectively.
Innovation Solution
The method involves tagging computation kernels, neurons, and layers with metadata to expose the entire computational graph to a hardware scheduler, enabling intelligent load balancing and predicting computational complexity, allowing for opportunistic parallelism and efficient job scheduling across resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If layer-fusion is used to expose multiple layers to simultaneous computation, then parallelism is improved, but device complexity increases and the approach becomes error-prone
Solution Approach 1:
The system automatically identifies and fuses layers that benefit from parallel execution without requiring explicit programmer intervention. The compiler autonomously analyzes the computational graph, determines fusion opportunities, and generates optimized code, making the system self-optimizing rather than manually configured.
Solution Approach 2:
The compiler performs static analysis of the entire computational graph beforehand to identify layers with sufficient computational demands and determine optimal fusion opportunities. This preliminary analysis enables the system to prepare fusion strategies in advance rather than making opportunistic decisions during execution.
2Productivity
If the entire computational graph is exposed to the compiler for layer-fusion, then parallelism is improved, but ease of operation deteriorates due to programming difficulty
Solution Approach 1:
The compiler automatically performs the complex task of analyzing the computational graph and identifying fusion opportunities without requiring the programmer to manually specify fusion points or expose the entire graph structure. The system handles the complexity internally while maintaining simple user interfaces.
3Device complexity
If traditional load balancing is used without metadata, then device complexity is reduced, but measurement precision deteriorates due to inability to predict computational complexity
Solution Approach 1:
The system attaches metadata parameters to computational kernels that encode information about computational complexity, data dimensions, and operation types. These parameters enable the load balancer to make informed scheduling decisions based on quantitative metrics rather than simple heuristic rules.
Data Source
AI summary
Methods and systems for load balancing in a neural network system using metadata are disclosed. Any one or a combination of one or more kernels, one or more neurons, and one or more layers of the neural network system are tagged with metadata. A scheduler detects whether there are neurons that are available to execute. The scheduler uses the metadata to schedule and load balance computations across compute resources and available resources.