Configurable Hardware Core for IoT Object Detection

Pedro R. Miranda, Daniel Pestana, João D. Lopes, Rui Policarpo Duarte, Mário P. Véstias, Horácio C. Neto and José T. de Sousa
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Pedro R. Miranda: INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal
Daniel Pestana: INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal
João D. Lopes: INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal
Rui Policarpo Duarte: INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal
Mário P. Véstias: INESC-ID, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1500-310 Lisboa, Portugal
Horácio C. Neto: INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal
José T. de Sousa: INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal

Future Internet, 2021, vol. 13, issue 11, 1-20

Abstract: Object detection is an important task for many applications, like transportation, security, and medical applications. Many of these applications are needed on edge devices to make local decisions. Therefore, it is necessary to provide low-cost, fast solutions for object detection. This work proposes a configurable hardware core on a field-programmable gate array (FPGA) for object detection. The configurability of the core allows its deployment on target devices with diverse hardware resources. The object detection accelerator is based on YOLO, for its good accuracy at moderate computational complexity. The solution was applied to the design of a core to accelerate the Tiny-YOLOv3, based on a CNN developed for constrained environments. However, it can be applied to other YOLO versions. The core was integrated into a full system-on-chip solution and tested with the COCO dataset. It achieved a performance from 7 to 14 FPS in a low-cost ZYNQ7020 FPGA, depending on the quantization, with an accuracy reduction from 2.1 to 1.4 points of m A P 50 .

Keywords: Internet of Things; object detection; YOLO; FPGA (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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