arXiv Machine Learning July 3, 2026 By Wan Song, Wei Zhou, Rui Wang, Jun Yu, Toru Kurihara, Jiajia Xu, Shu Zhan

WBMM: Windowed Batch Matrix Multiplication for Efficient Large Receptive Field Convolution

Read the original on arXiv Machine Learning →

arXiv:2607. 02097v1 Announce Type: cross Abstract: Large kernel depthwise convolutions achieve strong performance but suffer from significant degradation as kernel size grows due to irregular memory access from gather-based computation; while Large Kernel Acceleration (LKA) helps on small feature maps, it becomes counterproductive on large feature maps, even slower than non-accelerated implementations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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