Personalized Neural Network Pruning for Edge Memory Constraints
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Solution Overview
Problem
Conventional personalization methods for deep neural networks on edge devices are computationally burdensome due to the need for retraining or fine-tuning with limited personal data, which is impractical for memory-constrained devices.
Innovation Solution
Prototype-based personalized pruning (PPP) generates a personalized model by learning a metric space using a prototype representation, dynamically customizing the model structure to enhance personalization and reduce complexity without fine-tuning with personal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retraining or fine-tuning is performed with personal data, then personalization accuracy is improved, but computational cost increases substantially
Solution Approach 1:
The patent pre-computes and stores channel importance scores for all possible channels during an offline phase. When personalization is needed, the system only needs to retrieve these pre-computed scores and select channels based on the personal data prototype, avoiding the need for computationally expensive retraining or fine-tuning operations on the edge device.
Solution Approach 2:
The patent creates a simplified copy of the personalization process by using a prototype-based approach. Instead of copying the entire training process to the edge device, it uses a lightweight prototype representation that captures essential personal characteristics, which then guides channel selection from pre-computed importance scores.
2Measurement precision
If retraining or fine-tuning is performed with personal data, then personalization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent pre-computes and stores channel importance scores for all possible channels during an offline phase. When personalization is needed, the system only needs to retrieve these pre-computed scores and select channels based on the personal data prototype, avoiding the need for computationally expensive retraining or fine-tuning operations on the edge device.
Solution Approach 2:
The patent uses a lightweight prototype representation that can be quickly generated from personal data. This prototype is a simple computational object that captures essential personal characteristics without requiring complex models or extensive computational resources, making it suitable for resource-constrained edge devices.
3Reliability
If a generalized model with complex network is used, then model performance is improved, but memory requirements increase
Solution Approach 1:
The patent segments the neural network into individual channels and evaluates them independently using pre-computed importance scores. This allows the system to select only the most relevant channels for personalization, creating a smaller, more memory-efficient model that maintains performance by focusing on important features rather than using the complete complex network.
Solution Approach 2:
The patent applies different treatment to different channels of the network based on their importance scores. Instead of uniformly using all channels or uniformly pruning them, the system selectively activates only those channels with high importance scores for the given personal data prototype, optimizing memory usage while preserving performance-critical pathways.
Data Source
AI summary
A method for generating a personalized model includes receiving one or more personal data samples from a user. A prototype of a personal identity is generated based on the personal data samples. The prototype of the personal identity is trained to reflect personal characteristics of the user. A network graph is generated based on the prototype of the personal identity. One or more channels of a global network are pruned based on the network graph to produce the personalized model.