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research-article

Algorithm-Hardware Co-Design of Single Shot Detector for Fast Object Detection on FPGAs

Authors:

Sarma Vrudhula,

Jae-sun SeoAuthors Info & Claims

2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Pages 1 - 8

https://doi.org/10.1145/3240765.3240775

Published: 05 November 2018 Publication History

Abstract

The rapid improvement in computation capability has made convolutional neural networks (CNNs) a great success in recent years on image classification tasks, which has also prospered the development of objection detection algorithms with significantly improved accuracy. However, during the deployment phase, many applications demand low latency processing of one image with strict power consumption requirement, which reduces the efficiency of GPU and other general-purpose platform, bringing opportunities for specific acceleration hardware, e.g. FPGA, by customizing the digital circuit specific for the inference algorithm. Therefore, this work proposes to customize the detection algorithm, e.g. SSD, to benefit its hardware implementation with low data precision at the cost of marginal accuracy degradation. The proposed FPGA-based deep learning inference accelerator is demonstrated on two Intel FPGAs for SSD algorithm achieving up to 2.18 TOPS throughput and up to 3.3× superior energy-efficiency compared to GPU.

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Cited By

Kim YKim HRyu SDev KYoo JMeinerzhagen P(2024)Statues: Energy-Efficient Video Object Detection on Edge Security Devices with Computational SkippingProceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design10.1145/3665314.3670822(1-6)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1145/3665314.3670822
Ruiz-Beltrán CRomero-Garcés AGonzález-García MMarfil RBandera A(2023)FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance SystemElectronics10.3390/electronics1222471312:22(4713)Online publication date: 20-Nov-2023
https://doi.org/10.3390/electronics12224713
Vrbaski VJosic SVranjkovic VTeodorovic PStruharik R(2023)Puppis: Hardware Accelerator of Single-Shot Multibox Detectors for Edge-Based ApplicationsElectronics10.3390/electronics1222455712:22(4557)Online publication date: 7-Nov-2023
https://doi.org/10.3390/electronics12224557
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Algorithm-Hardware Co-Design of Single Shot Detector for Fast Object Detection on FPGAs

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cover image Guide Proceedings

2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Nov 2018

939 pages

Copyright © 2018.

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IEEE Press

Publication History

Published: 05 November 2018

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Cited By

Kim YKim HRyu SDev KYoo JMeinerzhagen P(2024)Statues: Energy-Efficient Video Object Detection on Edge Security Devices with Computational SkippingProceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design10.1145/3665314.3670822(1-6)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1145/3665314.3670822
Ruiz-Beltrán CRomero-Garcés AGonzález-García MMarfil RBandera A(2023)FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance SystemElectronics10.3390/electronics1222471312:22(4713)Online publication date: 20-Nov-2023
https://doi.org/10.3390/electronics12224713
Vrbaski VJosic SVranjkovic VTeodorovic PStruharik R(2023)Puppis: Hardware Accelerator of Single-Shot Multibox Detectors for Edge-Based ApplicationsElectronics10.3390/electronics1222455712:22(4557)Online publication date: 7-Nov-2023
https://doi.org/10.3390/electronics12224557
Kang H(2023)SSDLiteX: Enhancing SSDLite for Small Object DetectionApplied Sciences10.3390/app13211200113:21(12001)Online publication date: 3-Nov-2023
https://doi.org/10.3390/app132112001
Suh HMeng JNguyen TKumar VCao YSeo J(2023)Algorithm-hardware Co-optimization for Energy-efficient Drone Detection on Resource-constrained FPGAACM Transactions on Reconfigurable Technology and Systems10.1145/358307416:2(1-25)Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1145/3583074
Fan HLiu SQue ZNiu XLuk W(2023)High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating PointIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.311630234:8(4473-4487)Online publication date: Aug-2023
https://doi.org/10.1109/TNNLS.2021.3116302
Zhang LLv QGao DZhou XMeng WYang QZhuo C(2023)A fine-grained mixed precision DNN accelerator using a two-stage big–little core RISC-V MCUIntegration10.1016/j.vlsi.2022.10.00688(241-248)Online publication date: Jan-2023
https://doi.org/10.1016/j.vlsi.2022.10.006
Wang AYe YPeng YZhang DYan ZWang D(2023)A Low-Latency Hardware Accelerator for YOLO Object Detection AlgorithmsAdvanced Parallel Processing Technologies10.1007/978-981-99-7872-4_15(265-278)Online publication date: 8-Nov-2023
https://doi.org/10.1007/978-981-99-7872-4_15
Balasubramaniam APasricha S(2023)Object Detection in Autonomous Cyber-Physical Vehicle Platforms: Status and Open ChallengesMachine Learning and Optimization Techniques for Automotive Cyber-Physical Systems10.1007/978-3-031-28016-0_17(509-523)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-28016-0_17
Tesema SBourennane E(2022)Resource- and Power-Efficient High-Performance Object Detection Inference Acceleration Using FPGAElectronics10.3390/electronics1112182711:12(1827)Online publication date: 8-Jun-2022
https://doi.org/10.3390/electronics11121827
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