Heterogeneous NPU Evaluation for ANN Deployment Compatibility
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Solution Overview
Problem
The commercialization and deployment of neural processing units (NPUs) for artificial neural network (ANN) models face challenges due to a lack of information for selecting appropriate processors, uncertainty about model compatibility, and difficulty in predicting performance metrics such as power consumption and frame per second (FPS) when executed on specific NPUs.
Innovation Solution
A system and method for evaluating ANN models on a NPU farm using a user device, server, and ANN model processing device, which allows users to test NPUs online by uploading models and datasets, compile and instantiate them, and assess compatibility and performance, providing recommendations based on performance parameters like temperature profile, power consumption, and inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users want to select and deploy appropriate NPUs by testing them before purchase, then they can ensure compatibility and optimize performance, but they face a lack of information and uncertainty about model compatibility and performance metrics
Solution Approach 1:
The system performs preliminary actions by providing a simulation environment where users can test ANN models on NPUs before actual deployment. The server pre-compiles models and executes them on evaluation datasets to generate performance parameters in advance, allowing users to make informed selections without uncertainty about compatibility or performance metrics.
Solution Approach 2:
The server acts as an intermediary between users and NPUs. It receives model files and evaluation datasets from users, executes them on the NPU farm, and returns performance parameters. This intermediary service provides the necessary information about compatibility and performance without requiring users to directly test on physical hardware.
2Measurement precision
If the system provides comprehensive testing and evaluation capabilities for NPUs, then users can assess compatibility and performance accurately, but the system complexity increases with multiple neural processors of different configurations
Solution Approach 1:
The system segments the NPU farm into multiple processors of different configurations (first neural processor, second neural processor, etc.), each suited for specific model types. The server independently evaluates each processor's performance on the ANN model using evaluation datasets, generating separate performance parameters for each configuration. This segmentation allows precise measurement of compatibility and performance for different NPU types without overwhelming complexity.
Solution Approach 2:
The system changes parameters such as model architecture, dataset characteristics, and evaluation metrics to comprehensively assess NPU performance. By varying these parameters across different evaluation scenarios, the system generates detailed performance parameters that accurately reflect compatibility and performance for each NPU configuration, enabling precise measurements despite hardware diversity.
3Productivity
If the system instantiates and executes ANN models on multiple neural processors, then performance can be optimized through compilation options, but the processing time and computational resources required increase
Solution Approach 1:
The system creates a virtual copy of the NPU evaluation environment through software simulation. Users can upload their ANN model files and evaluation datasets to the server, which then executes simplified versions of the evaluation process. This copying approach allows performance assessment without requiring physical hardware resources, significantly reducing evaluation time while maintaining accuracy.
Solution Approach 2:
The server performs preliminary compilation and execution of ANN models on evaluation datasets before users need to make deployment decisions. By pre-executing models on the NPU farm and generating performance parameters in advance, the system eliminates the need for time-consuming real-time testing, allowing users to quickly review results and make informed selections without significant time loss.
Data Source
AI summary
A method for evaluating artificial neural network (ANN) model's processing performance comprising selecting a type and a number of at least one neural processing unit (NPU) for processing performance evaluation for a user, selecting at least one of a plurality of compilation options for an artificial neural network (ANN) model to be processed by the at least one NPU which is selected, uploading the ANN model and at least one evaluation dataset to be processed by the at least one NPU which is selected, compiling the ANN model according to the at least one of the plurality of compilation options which is selected, and reporting a processing performance by processing the ANN model on the at least one NPU which is selected.