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Coding What Matters: A Semantic-Aware Memory Interface for Energy-Efficient Perception in Autonomous Vehicles

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Autonomous vehicles stream high-resolution surround-camera frames into memory before perception runs. This sensor-to-memory path consumes energy when cells store ones and adjacent bytes toggle on the data bus, so its cost follows bit-1 density and switching activity rather than pixel semantics. We present MotiMem-Omega, a semantic-aware memory-interface coder that lowers this cost while preserving perception predictions. Its semantic importance field protects traffic participants, especially vulnerable road users, while assigning lower fidelity to sky and empty background. Cross-dataset bit-se

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