Transformer-Based Neural Network for Hardware Performance Prediction
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Solution Overview
Problem
Existing methods for predicting software performance on hardware platforms are time-consuming and unable to capture correlations among operations, and hardware vendors may be reluctant to share proprietary information through lookup tables.
Innovation Solution
A transformer-based neural network system that receives a compiled neural network model, performs feature embedding, applies positional encoding and attention functions, and reduces dimensions to output performance metrics such as latency and power consumption, while protecting proprietary hardware information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lookup table is used for performance prediction, then performance estimation can be provided to developers, but constructing the lookup table is time-consuming and it cannot capture correlations among operations
Solution Approach 1:
The patent replaces the traditional mechanical lookup table approach with a transformer-based neural network system. The transformer model learns complex patterns and correlations among operations during training, then performs fast inference without requiring manual construction of lookup tables. This substitution enables the system to capture operational correlations while providing ultra-fast performance predictions.
Solution Approach 2:
The patent transforms the performance prediction problem by changing from discrete lookup table entries to continuous neural network predictions. The transformer model processes operation sequences and generates performance metrics as continuous outputs, allowing it to generalize to unseen operation combinations rather than relying on pre-computed discrete values.
2Measurement precision
If a lookup table is provided by hardware vendor, then performance measurements are available, but the vendor cannot safeguard proprietary hardware information
Solution Approach 1:
The patent introduces a transformer-based prediction system as an intermediary between hardware vendors and software developers. The model is trained on vendor-specific hardware data but then deployed independently, allowing vendors to protect their proprietary information while still enabling developers to access performance prediction capabilities through the trained model.
Solution Approach 2:
The patent creates a computational copy of hardware performance characteristics through the trained transformer model. Instead of sharing actual hardware data or lookup tables, the vendor provides a trained model that replicates performance prediction functionality, thereby protecting proprietary information while maintaining prediction accuracy.
3Device complexity
If traditional performance prediction methods are used, then simple estimation is possible, but they cannot capture correlations among operations
Solution Approach 1:
The patent employs a dynamic transformer-based neural network that can adaptively learn and capture correlations among operations. The self-attention mechanism dynamically weights the importance of different operations based on their contextual relationships, allowing the system to model complex inter-dependencies while maintaining computational efficiency during inference.
Data Source
AI summary
A prediction engine predicts the performance of a neural network model executed on a hardware platform. The neural network model is compiled for the hardware platform. The neural network model includes multiple layers and each layer is defined by a set of operations and corresponding configuration settings of the operations. For each layer, the prediction engine performs feature embedding on the set of operations and the corresponding configuration settings to generate a feature embedded sequence of categorical feature vectors and numerical feature vectors. Positional encoding and a series of attention functions are applied on the feature embedded sequence to generate an encoded sequence. The prediction engine reduces the dimensions of the encoded sequence to output a performance metric of executing the neural network model on the hardware platform.