FieldRT: Reconfigurable Architecture from Geometric Sensitivity to Streaming Acceleration

arXiv:2611.00476v1 Announce Type: new Abstract: Geometric perception on edge must simultaneously achieve two goals that are rarely addressed together: preserve accurate spatial understanding for object localization and operate within stringent latency, memory, energy, and precision constraints. This coupling is particularly evident in oriented bird's-eye-view (BEV) detection, where global context, multi-scale interactions, and geometric fusion affect both prediction quality and hardware dataflow. Conventional deployment flows first compress a model using aggregate quality or complexity proxies and then map the compressed operations to a fixed accelerator interface.