ANN Model Evaluation on NPUs Through Compilation Options
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Solution Overview
Problem
There is a lack of information for selecting appropriate neural processors to process user-developed artificial neural network models, and commercialization of neural processing units (NPUs) is in its infancy, with uncertainties regarding compatibility and performance when executing user-developed ANN models on specific NPUs due to differences in model size, layer count, and special functions.
Innovation Solution
A system and method for evaluating ANN models on neural processors (NPUs) by compiling and instantiating them on selected NPUs, assessing compatibility and performance using evaluation datasets, and providing recommendations based on performance parameters such as power consumption, FPS, and inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural processors are provided with different configurations to handle various ANN model sizes and complexities, then the adaptability and versatility of the system is improved, but the device complexity increases
Solution Approach 1:
The neural processor is divided into multiple configuration types (first neural processor and second neural processor with different configurations) to handle different ANN model complexities. This segmentation allows the system to adapt to various model sizes and layer counts by selecting appropriate processor configurations, thereby improving adaptability while managing device complexity through modular design.
Solution Approach 2:
The system provides a universal evaluation framework that can assess multiple neural processor configurations against various ANN models using the same evaluation datasets and performance parameters. This multi-functionality allows a single system to serve multiple purposes: evaluating different processor types, supporting various model complexities, and providing comprehensive performance assessments without requiring separate specialized systems for each task.
2Measurement precision
If comprehensive performance evaluation is conducted on multiple neural processors with different configurations, then the measurement precision of performance parameters is improved, but the loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining evaluation datasets, performance parameters, and processor configurations before actual evaluation. Users can select from pre-configured evaluation options and processor types, avoiding the need to set up evaluation frameworks from scratch. This preliminary preparation enables comprehensive performance measurement while reducing the time required to initiate and execute evaluations.
Solution Approach 2:
The system incorporates feedback mechanisms where performance parameters are measured and evaluated, and results are used to provide recommendations on optimal processor selections. This feedback loop allows for iterative refinement of evaluation results and can guide users in making informed decisions about processor selection based on actual performance data, improving measurement precision while managing evaluation time through result-driven approaches.
3Productivity
If multiple compilation options and algorithms are applied to optimize ANN models, then the productivity of model processing is improved, but the device complexity increases
Solution Approach 1:
The system applies parameter changes through various compilation options including quantization algorithms, pruning algorithms, retraining algorithms, model compression algorithms, AI-based optimization algorithms, and knowledge distillation algorithms. These parameter changes modify model characteristics (precision, size, complexity) to optimize processing productivity. The system manages compilation complexity by providing structured options and automatic configuration based on selected processor types and model characteristics.
Data Source
AI summary
A method for evaluating processing performance of an artificial neural network (ANN) model is disclosed. The method includes receiving an ANN model and receiving a selection of a first subset of neural processors from among a plurality of neural processors for instantiating at least one layer of the ANN model. A plurality of compilation options is received to modify the ANN model for instantiation on the selected subset. The at least one layer of the ANN model is instantiated on the selected subset by compiling the ANN model according to the compilation options. The instantiated layer processes one or more evaluation datasets. One or more performance parameters associated with the processing are generated by one or more operating processors. The generated performance parameters are transmitted over a network to facilitate analysis of ANN model execution efficiency under selected processor and compilation configurations.