AHC introduces a meta-learned adaptive compression technique for continual object detection on memory-constrained microcontrollers. The approach dynamically adjusts compression parameters during inference to balance detection accuracy and memory footprint. This enables efficient deployment of vision models on embedded edge devices.
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AHC: Meta-Learned Adaptive Compression for Continual Object Detection on Memory-Constrained Microcontrollers
Meta-learned compression dynamically adapts model inference to squeeze object detection onto memory-starved microcontrollers, balancing accuracy against extreme resource constraints on edge hardware.
Tuesday, April 14, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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