arXiv Machine Learning
Sep 25

Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge

Albireo is an adaptive, energy‑efficient inference framework for video object detection on edge devices that wraps existing detectors without modification. It uses a 10‑dimensional Kalman filter per active object to decide when to skip detector calls, predicting bounding boxes on skipped frames at near‑zero GPU cost. Evaluated on BDD100K with YOLO and RF‑DETR detectors on NVIDIA Jetson AGX Thor and Orin, Albireo maintains AP@50 within ±1.2 pp of full‑frame inference while reducing energy consumption by 12.1–17.6 % and improving accuracy for some models.

By Amir Taherin, Jos\'e Cano, Bin Ren, Yanzhi Wang, David Kaeli
arXiv Computer Vision
Oct 16

FALCON: FPGA-Accelerated Latency-Certifiable Occupancy Networks for Camera-Based Autonomous Driving

FALCON is a camera‑based 3D occupancy front‑end built on the AMD Kria KR260 FPGA that provides explicit latency guarantees for autonomous driving perception. The system uses cycle‑bounded programmable‑logic compute, a measured inference envelope over 100k frames, and a watchdog to flag stale outputs, achieving all 300k proxy frames within a 100 ms deadline plus at most 10.2 ms. On the full FlashOcc pipeline the board attains 29.88 mIoU on the Occ3D‑nuScenes set, matching 92.8 % of an FP32 baseline, while maintaining tighter tail latency than competing Jetson Orin and datacenter GPUs.

By Haohua Que, Mingkai Liu, Handong Yao