Please use this identifier to cite or link to this item:
Title: Deep reinforcement learning-based autonomous parking design with neural network compute accelerators
Authors: Özeloğlu, Alican
Gürbüz, İsmihan Gül
Şan, İsmail
Keywords: autonomous parking
Deep Q-Learning
high-level synthesis
neural network accelerator
Issue Date: 2021
Publisher: Wiley
Abstract: We describe the design and implementation of an autonomous prototype vehicle which finds an empty parking slot in a parking area, and parks itself in the empty parking slot, using neural networks based on deep reinforcement learning (RL). To perform an autonomous parking procedure for our prototype vehicle, two different artificial neural networks (ANNs) are trained using a deep RL Algorithm in a simulation environment and embedded into the computing platform of the prototype car. One of the ANNs enables the vehicle to drive autonomously in the parking environment. At the same time, an image processing algorithm is used to determine whether a parking slot is empty. When the image processing algorithm finds a suitable parking slot, a different ANN is activated and performs a safe parking procedure. However, ANN-based machine learning techniques require high processing power and impose a high computational burden on embedded CPU and GPU platforms. To alleviate the computational burden, one can achieve higher performance and less power consumption using an application-specific hardware design, where logic resources are fully exploited according to the algorithm of interest, in an energy-efficient manner. In this article, hardware accelerators for our ANN models are designed and generated via the Vivado high-level synthesis (HLS) tool, targeting an ARM based programmable SoC platform, ZedBoard. Our ANN accelerators have achieved a speedup of 17x as compared to an ARM software implementation. For deeper fully-connected layers used in deep RL-based solutions, function-level parallelism (Vivado's dataflow) is employed to improve the computational efficiency. Our proposed stage-level description for fully connected layers outperforms recent studies in terms of computation time.
ISSN: 1532-0626
Appears in Collections:Elektrik-Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

Show full item record

CORE Recommender


checked on Dec 28, 2022

Page view(s)

checked on Oct 3, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.