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AI Research
arXiv CS.AI·Jun 10·5 min read
Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters
TL;DR
Deep neural networks often struggle with memory constraints on edge devices due to high off-chip weight transfer during inference. Sigma-Branch (SigmaB) is a framework that restructures a pretrained dense network into a hierarchical binary tree, allowing for reduced active parameters during inference.
✦ Why It Matters
Engineers can leverage SigmaB to optimize deep learning models for efficient deployment on edge devices without sacrificing performance.
Key Takeaways
- Implement SigmaB for efficient edge device deployment.
- Utilize hierarchical structures to reduce active parameters.
- Explore activation-based clustering for weight distribution.
- Achieve performance close to dense networks with fewer active parameters.
Full Summary
Deploying deep neural networks on edge devices is limited by the need to transfer weights from off-chip memory, which can be inefficient. Sigma-Branch (SigmaB) addresses this by reorganizing a pretrained dense network into a hierarchical binary tree structure, consisting of a shared backbone, hierarchical routers, and specialized leaves.
The method employs activation-based spherical k-means clustering to distribute pretrained weights across the tree, followed by soft-routing fine-tuning to optimize each leaf for its specific input subset. During inference, SigmaB executes a single path from root to leaf, significantly reducing the number of active parameters while keeping the complete set of dense parameters in memory.
Experiments on datasets like CIFAR-100 and ImageNet-1K demonstrated a 58-60% reduction in active parameters, outperforming traditional static pruning methods by 14-23 percentage points in terms of active parameter efficiency. This framework decouples memory traffic during inference from the total parameter count, offering a promising solution for deploying deep learning models on resource-constrained devices.