---
res:
  bibo_abstract:
  - Edge-based Artificial Intelligence (AI) acceleration has recently improved progress
    in real-time object detection. Object detection on edge devices requires a balance
    between accuracy, speed, and power efficiency. This paper proposes a customized
    Deep Learning Processor Unit (DPU)-aware architecture for attention-based YOLO
    variants deployed on AMD FPGAs. Specifically, we evaluate and benchmark YOLOv26
    and YOLOv11, two modern attention-based YOLO variants, on the Xilinx ZCU104 across
    both standard and oriented object detection tasks. We replace unsupported activation
    functions, substitute split operations with 1x1 convolutions, and approximate
    the spatial attention mechanism in a DPU-compatible way. All models are then trained
    and evaluated across six benchmark datasets such as COCO, Pascal VOC, KITTI, DOTA,
    DIOR-R, and an in-house human presence dataset, and benchmarked across all eight
    DPU configurations (B512 to B4096) in terms of mAP, FPS, latency, power, and resource
    utilization. Notably, YOLOv26n and YOLOv26n-OBB deliver the highest end-to-end
    throughput at 34.05 and 29.55 FPS for standard and oriented detection, respectively,
    with an average of 5% absolute reduction in accuracy due to quantization while
    achieving up to approximately 3x lower power consumption compared with the state
    of the art.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Suraj
      foaf_name: Karki, Suraj
      foaf_surname: Karki
  - foaf_Person:
      foaf_givenName: Qazi Arbab
      foaf_name: Ahmed, Qazi Arbab
      foaf_surname: Ahmed
      foaf_workInfoHomepage: http://www.librecat.org/personId=257333
    orcid: 0000-0002-1837-2254
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-1837-2254/work/227259862
  - foaf_Person:
      foaf_givenName: Thorsten
      foaf_name: Jungeblut, Thorsten
      foaf_surname: Jungeblut
      foaf_workInfoHomepage: http://www.librecat.org/personId=242294
    orcid: 0000-0001-7425-8766
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0001-7425-8766/work/227259884
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_title: 'No Attention, No Problem: DPU-Aware Attention Approximation in Modern
    YOLO on FPGA@'
...