Group-Shared Low-Rank Approximation for Mobile-Efficient Pointwise Convolutions in Large-Kernel CNNs
Score
72首次系统指出并解决大核CNN中逐点卷积的部署瓶颈，提供新的群共享低秩视角。
方法设计严谨，实验覆盖多个主流大核CNN模型，对比充分，论证有据。
直接面向移动端存储和内存带宽限制，可落地性强，对边缘AI部署具有明确工程价值。
边缘端大模型部署是当前热点，但论文以arXiv预印本形式发布，尚待同行评议。
来自arXiv AI/CL/LG预印本，作者机构未披露，但方法论与实验可信度高。
Base 80.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
Strong methodological novelty, addresses a practical deployment pain point, covers multiple mainstream models with solid evidence, and offers direct reference for mobile AI engineering.
AI commentary
「This paper offers a crucial insight: while the field focuses on compressing depthwise convolutions, pointwise convolutions are the real heavyweight. For engineers working on on-device deployment, CGS group-sharing is a simple yet effective idea worth trying on their own models.」
Quick overview
Large-kernel CNNs achieve excellent performance in vision tasks but suffer quadratic parameter growth, hindering edge deployment. Existing efficient architectures compress depthwise convolutions while overlooking that pointwise convolutions account for over 87% of parameters. This paper proposes a Channel Group-Shared (CGS) low-rank approximation based on SVD, sharing down/up projection matrices across channel groups and using group-specific scaling diagonals, significantly reducing parameters and enabling deployment of RepLKNet, ConvNeXt, SLaK on edge devices.
- Identifies pointwise convolutions as the dominant parameter bottleneck (>87%) in large-kernel CNNs
- Proposes Channel Group-Shared (CGS) low-rank approximation structurally isomorphic to SVD, sharing projection matrices
- Demonstrates favorable accuracy-storage trade-offs on RepLKNet, ConvNeXt, and SLaK
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