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arXiv Machine Learning By Oliver Busemann (Osnabr\"uck University, CLAAS E-Systems GmbH), Torben T\"oniges (CLAAS E-Systems GmbH), Martin Atzmueller (Semantic Information Systems Group, Osnabr\"uck University, German Research Center for Artificial Intelligence)

Memory Bandwidth, Not FLOPs: Profiling and Accelerating Query-Based Segmentation Decoders on Edge GPUs

Read the original on arXiv Machine Learning →

The paper shows that query‑based segmentation decoders on edge GPUs are limited by memory bandwidth rather than FLOPs. Profiling reveals that non‑tensor‑core operations consume a large fraction of decoder time and bytes, saturating the DRAM bus. CompactInst mitigates this by using parameter‑free spatial pooling and a lightweight refinement head, achieving significant speedups on Jetson Orin Nano while maintaining accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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