Compression Profile Search for Neural Network Pruning Tradeoffs
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Solution Overview
Problem
The increasing complexity and scale of neural networks require more computational resources, limiting their application in systems with processing limitations, such as memory, bandwidth, and latency constraints, necessitating techniques to compress neural networks without significant accuracy loss.
Innovation Solution
The implementation of compression profiles and policies, such as channel pruning, using predefined profiles specific to network architectures, allows for quick and accurate compression of neural networks, reducing resource requirements and enabling deployment on resource-constrained devices, through techniques like randomization and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are scaled up to address more complex problems, then problem-solving capability is improved, but computational resource requirements increase
Solution Approach 1:
The patent extracts and removes unnecessary or redundant neural network units through pruning techniques. By identifying and eliminating less important connections and nodes, the system reduces the overall complexity and resource requirements while preserving the essential problem-solving capabilities of the network.
Solution Approach 2:
The patent applies different pruning strategies to different parts of the neural network based on their specific characteristics. Importance scoring is calculated locally for each unit or connection, allowing tailored compression decisions that optimize the balance between resource reduction and performance maintenance in different network regions.
2Use of energy by moving object
If neural networks are compressed to reduce resource requirements, then memory and power usage are reduced, but model accuracy may deteriorate
Solution Approach 1:
The patent employs feedback mechanisms where the impact of pruning decisions is evaluated through importance scoring and performance monitoring. The system continuously assesses which units contribute most to accurate predictions and uses this feedback to guide subsequent pruning decisions, ensuring that accuracy-critical components are preserved while compressing the model.
Solution Approach 2:
The patent changes key parameters such as pruning thresholds, importance scoring weights, and compression ratios to optimize the balance between model size and accuracy. By adjusting these parameters based on performance requirements and resource constraints, the system can adaptively control the trade-off between compression level and model reliability.
3Quantity of substance
If traditional compression techniques are applied to neural networks, then model size is reduced, but the compression process requires significant time and computational cost
Solution Approach 1:
The patent performs preliminary importance assessment and prioritization of neural network units before the actual compression process. By pre-calculating importance scores and identifying low-priority components ahead of time, the system streamlines the compression process and reduces the computational effort required during the actual model reduction phase.
Solution Approach 2:
The patent uses surrogate models or simplified copies to estimate the impact of pruning decisions without requiring extensive computational resources on the full model. By working with compressed representations or proxy evaluations, the system can assess compression effects quickly and make informed decisions without time-consuming full-model evaluations.
Data Source
AI summary
Compression profiles may be searched for trained neural networks. An iterative compression profile search may be performed response to a search request. Different prospective compression profiles may be generated for trained neural networks according to a search policy. Performance of compressed versions of the trained neural networks according to the compression profiles may be tracked. The search policy may be updated according to an evaluation of the performance of the compression profiles for the compressed versions of the trained neural networks using compression performance criteria. When a search criteria is satisfied, a result for the compression profile search may be provided.