CONet: Channel Optimization for Convolutional Neural Networks

Abstract

The number of channels in convolutional neural networks significantly impacts both performance and computational cost. CONet presents a systematic approach to optimizing channel configurations for improved efficiency without sacrificing accuracy.

Key Contributions

1. Channel Optimization Framework: We introduce a principled approach to determining optimal channel widths across network layers.

1. Efficiency Gains: Our method achieves comparable accuracy with significantly reduced computational requirements.

1. Architecture Insights: We provide insights into how channel configurations affect feature learning.

The Channel Width Problem

• Traditional architectures use heuristic channel progressions (e.g., doubling at each stage)
• Suboptimal channel allocation wastes computational resources
• Different layers have different capacity requirements

Methodology

• Analyze feature utilization across layers
• Optimize channel allocation based on layer importance
• Maintain accuracy while reducing FLOPs

Key Results

CONet-optimized architectures achieve: - Similar accuracy to baseline models - Significant reduction in parameters - Faster inference times

Applications

The techniques are applicable to any CNN architecture and are particularly valuable for deployment on resource-constrained devices such as mobile phones and edge devices.