Embedded Neural Network Pipeline for Pre-Training Performance Estimation
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Solution Overview
Problem
Training machine-learned models for autonomous control systems is time-consuming and requires extensive exploration of different architectures, with existing methods often failing to optimize performance across various platforms due to varying hardware capabilities.
Innovation Solution
A pipeline that generates an intermediate representation of models, allowing for performance estimation without training, and iteratively modifies model complexity based on target platform capabilities, using a virtual machine to execute the models efficiently across different platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional model training methods are used for autonomous control systems, then model accuracy can be achieved, but training time becomes excessively long and resource-intensive
Solution Approach 1:
The system performs preliminary actions by generating an intermediate representation of the model architecture before actual training begins. This includes creating a computational graph, estimating memory requirements, and determining operation schedules in advance. By preparing these structural elements beforehand, the system avoids time-consuming trial-and-error during the training phase, thus reducing overall training time while maintaining model accuracy.
Solution Approach 2:
The patent introduces an intermediate representation as a mediator between the model architecture definition and the actual training process. This intermediate form includes a computational graph that represents operations and data flows, allowing the system to analyze and optimize model structure without full training. The virtual machine executes this intermediate representation to estimate performance metrics, serving as a bridge that enables early validation and optimization.
2Adaptability or versatility
If multiple model architectures are explored for different platforms, then platform-specific optimization is achieved, but the complexity of the deployment process increases
Solution Approach 1:
The system implements a universal intermediate representation that can represent model architectures independently of the target platform. This computational graph format serves multiple functions: it can be used for memory estimation, operation scheduling, performance prediction, and deployment across different embedded platforms. By maintaining this single universal representation, the system achieves platform-specific optimization without requiring separate model versions for each platform, thereby reducing deployment complexity.
Solution Approach 2:
The system optimizes model deployment by changing parameters in the intermediate representation based on target platform characteristics. The virtual machine analyzes platform-specific constraints (memory, compute capability) and adjusts the computational graph accordingly, such as modifying operation schedules or memory allocations. This parameter adjustment approach allows the same base model to be adapted to different platforms without redesigning the entire architecture.
3Ease of manufacture
If model complexity is reduced to meet embedded system constraints, then deployment feasibility improves, but model performance may deteriorate
Solution Approach 1:
The system applies partial optimization by selectively modifying only those parts of the model that are necessary to meet embedded system constraints. The intermediate representation allows the virtual machine to identify specific operations or layers that can be optimized (e.g., reducing precision for certain computations, reordering operations) without affecting the overall model architecture. This partial action approach maintains model performance while achieving deployment feasibility.
Solution Approach 2:
The system uses feedback from the virtual machine's performance estimation to guide model optimization. The virtual machine executes the intermediate representation and provides feedback on memory usage, operation counts, and other metrics. Based on this feedback, the system iteratively adjusts the computational graph or model parameters, comparing performance against constraints until both deployment feasibility and model performance are satisfied.
Data Source
AI summary
A model training and implementation pipeline trains models for individual embedded systems. The pipeline iterates through multiple models and estimates the performance of the models. During a model generation stage, the pipeline translates the description of the model together with the model parameters into an intermediate representation in a language that is compatible with a virtual machine. The intermediate representation is agnostic or independent to the configuration of the target platform. During a model performance estimation stage, the pipeline evaluates the performance of the models without training the models. Based on the analysis of the performance of the untrained models, a subset of models is selected. The selected models are then trained and the performance of the trained models are analyzed. Based on the analysis of the performance of the trained models, a single model is selected for deployment to the target platform.