Neural Network Pruning for Latency and Accuracy Trade-offs
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Solution Overview
Problem
Large neural networks require significant processing capacity and time, often exceeding latency and performance requirements in applications like real-time data transmission, due to their large size and complexity, and existing simplification methods do not effectively balance accuracy and performance.
Innovation Solution
A neural network pruning method that reduces the network size by removing neurons and layers based on predicted performance impact and importance scores, using hardware-aware latency pruning to optimize latency while maintaining accuracy, formulated as a resource allocation optimization problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural network size is reduced by removing neurons and layers, then processing capacity and inference speed are improved, but network accuracy deteriorates
Solution Approach 1:
The patent extracts and removes specific neurons and layers from the neural network based on their calculated importance scores and performance impact. By selectively taking out less important components, the network size is reduced while minimizing accuracy loss, directly resolving the contradiction between inference speed and accuracy.
Solution Approach 2:
The patent changes the parameters of the neural network by adjusting the number of neurons and layers based on performance metrics. Through iterative pruning that monitors accuracy and speed parameters, the network is optimized to achieve the desired balance between inference speed and accuracy.
2Device complexity
If existing simplification methods are used to reduce operations, then network size is reduced, but performance requirements are not satisfied or accuracy is compromised
Solution Approach 1:
The patent implements a feedback mechanism where the pruning process continuously monitors both accuracy and performance metrics. Based on this feedback, the pruning algorithm adjusts which neurons and layers to remove, ensuring that performance requirements are satisfied while reducing network size. The iterative process refines the pruned network until both size reduction and performance targets are achieved.
Solution Approach 2:
The patent makes the pruning process dynamic by iteratively adjusting the network structure based on measured performance. Rather than using static simplification rules, the system dynamically determines which components to remove based on real-time accuracy and speed measurements, allowing the network to adaptively meet performance requirements.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to simplify neural networks. In at least one embodiment, one or more portions of one or more neural networks are cause to be removed based, at least in part, on one or more performance metrics of the one or more neural networks.