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UID:dac_DAC 2026_sess317_RESEARCH1878@linklings.com
SUMMARY:Hardware-Aware Neural Architecture Search for Real-Time BEV-Based 
 3D Object Detection
DESCRIPTION:Jisup Lee, Soonhoi Ha, and Jeongwon Her (Seoul National Univer
 sity)\n\nBird's-eye-view (BEV) fusion of multi-modal sensor data is a lead
 ing approach for high-performance 3D object detection. However, state-of-t
 he-art models rarely exceed 10 FPS even on powerful GPUs, limiting their u
 se in real-time applications. In this paper, we present a neural architect
 ure search (NAS) framework that identifies models achieving both high accu
 racy and real-time inference. The search space is explicitly designed with
  hardware constraints to balance accuracy and computational efficiency on 
 resource-limited platforms. To better align LiDAR and camera features in t
 he BEV conversion module, we introduce a lightweight depth estimation netw
 ork and a LiDAR–camera cross-attention mechanism that enhances detection a
 ccuracy with minimal overhead. On the challenging nuScenes benchmark, our 
 model achieves 70.1% mAP, which represents a 2.3% point improvement over t
 he baseline model, while running at 35.6 FPS on the Nvidia Jetson AGX Orin
 , demonstrating its suitability for real-time autonomous driving.\n\nTrack
 : EDA7-II. Physical Design and Verification, Design\n\nSession Chairs: Chr
 istian Pilato (Politecnico di Milano); Mohsen Imani (University of Califor
 nia, Irvine); Flavio Ponzina (San Diego State University); Amin Farshidi (
 Cadence Design Systems, Inc.); Yonggan Fu (Georgia Institute of Technology
 , NVIDIA); Dimitrios Tsaras (Huawei, Hong Kong University of Science and T
 echnology (HKUST)); Anh Nguyen (Fujitsu Limited); Ruiyang Qin (Villanova U
 niversity); Jiliang Zhang (Hunan University); Meng Li (Peking University);
  and Rassul Bairamkulov (Advanced Micro Devices (AMD))\n\n
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