Teacher-Student Model Training for Resource-Constrained IoT Devices
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Solution Overview
Problem
Machine learning models with complex structures require significant resources, making them unsuitable for platforms with limited resources like home appliances and IoT devices, and existing model compression techniques do not ensure high accuracy on these devices.
Innovation Solution
A system and method for model compression that divides a teacher model into blocks and generates a student branch to train a student model based on the teacher model's outputs, allowing for efficient training and deployment on resource-constrained devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model has a complex structure for more accurate results, then accuracy is improved, but resource consumption increases making it unsuitable for platforms with limited resources
Solution Approach 1:
The teacher model is divided into multiple teacher blocks, and corresponding student blocks are created to form a segmented student model. This segmentation allows the complex teacher model to be broken down into manageable parts that can be efficiently implemented on resource-constrained devices while maintaining accuracy through progressive knowledge transfer from each teacher block to its corresponding student block.
Solution Approach 2:
A student model is created as a simplified copy of the teacher model structure. The student model replicates the essential architecture and functionality of the teacher model but with reduced complexity, allowing it to run on resource-limited platforms while still achieving high accuracy through knowledge distillation from the teacher model during training.
2Use of energy by moving object
If model compression is used to provide a lightweight machine learning model for limited resources, then resource efficiency is improved, but accuracy may be compromised
Solution Approach 1:
The student branch acts as an intermediary between the teacher blocks and the final student model. It receives features from teacher blocks, processes them through corresponding student blocks, and generates outputs that guide the training of the student model. This intermediary structure ensures that knowledge is effectively transferred from the resource-intensive teacher model to the efficient student model, maintaining accuracy while achieving compression.
Solution Approach 2:
The training process utilizes parameter changes by adjusting the weights and configurations of student blocks based on the outputs from teacher blocks. During training, the student model's parameters are optimized to match the teacher model's behavior on various inputs, allowing the compressed student model to achieve high accuracy despite having fewer parameters and lower resource requirements.
3Use of energy by moving object
If a student model is trained to mimic a teacher model, then resource efficiency is improved, but training complexity increases
Solution Approach 1:
The training process is segmented into multiple stages corresponding to different teacher blocks. Each student block is trained sequentially or in parallel with its corresponding teacher block, breaking down the complex training process into manageable segments. This segmentation reduces training complexity by allowing incremental learning and easier debugging compared to training the entire model at once.
Solution Approach 2:
The student branch receives features from only certain teacher blocks rather than all blocks, implementing partial action. This selective feature reception reduces the training complexity and computational burden during the knowledge distillation process, while still achieving effective knowledge transfer to the student model for deployment on resource-constrained devices.
Data Source
AI summary
A computer-implemented method of training a teacher model and a student model includes dividing the teacher model into a series of teacher blocks each comprising at least one layer; generating a first student branch receiving a first feature output from a first teacher block among the series of teacher blocks; training the teacher model based on outputs of the teacher model and the first student branch; and training the student model comprising a series of student blocks based on the trained teacher model, wherein the first student branch includes at least one student block.