A CUDA-based implementation of convolutional neural network

Choi, Seungjin; Lee, Kwang-Yeob

doi:10.1109/caipt.2017.8320682

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Advances in technology also led to better computers that are able to compute high processing power. New parallel computing platforms, such as Compute Unified Device Architecture (CUDA) [ 35 ] created by Nvidia, enable high-level processing to be done in a shorter period. With the development of NVIDIA CUDA Deep Neural Network [ 36 ], highly tuned implementations for forward and backward propagations are required in deep learning network models, allowing computation can be done on GPU at a much faster rate.…”

Section: Introductionmentioning

confidence: 99%

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Ramalingam

et al. 2021

Sensors

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Regular washing of public pavements is necessary to ensure that the public environment is sanitary for social activities. This is a challenge for autonomous cleaning robots, as they must adapt to the environment with varying pavement widths while avoiding pedestrians. A self-reconfigurable pavement sweeping robot, named Panthera, has the mechanisms to perform reconfiguration in width to enable smooth cleaning operations, and it changes its behavior based on environment dynamics of moving pedestrians and changing pavement widths. Reconfiguration in the robot’s width is possible, due to the scissor mechanism at the core of the robot’s body, which is driven by a lead screw motor. Panthera will perform locomotion and reconfiguration based on perception sensors feedback control proposed while using an Red Green Blue-D (RGB-D) camera. The proposed control scheme involves publishing robot kinematic parameters for reconfiguration during locomotion. Experiments were conducted in outdoor pavements to demonstrate the autonomous reconfiguration during locomotion to avoid pedestrians while complying with varying pavements widths in a real-world scenario.

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Section: Introductionmentioning

confidence: 99%

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Ramalingam

et al. 2021

Sensors

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“…In recent years, new technologies have pushed forward computational power and led to the development of parallel computing [29], which allows computation to be done in a shorter time. Some examples are the development of Compute Unified Device Architecture (CUDA) [30] and NVIDIA CUDA Deep Neural Network (CuDNN) [31], which allows computers to process deep learning computations with Graphic Processing Units at a much faster rate. Besides, with the advances in the Internet of Things (IoT) and cloud computing [32] where the processing of high computational power algorithms required by robots can be executed online on readily available technologies such as Google Colab, Amazon Web Services (AWS) or Microsoft Azure [33], segmented segmentation has become much more reliable and feasible for autonomous robots.…”

Section: Introductionmentioning

confidence: 99%

Reconfiguration During Locomotion by Pavement Sweeping Robot With Feedback Control From Vision System

Hayat

et al. 2020

IEEE Access

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Routine cleaning the pavement is an essential requirement to maintain a sustainable environment for social life. The different width and type of pavements raise the challenges for autonomous vehicles with fixed shape to operate effectively. In this paper, we introduce the vision based reconfiguration of self-reconfigurable pavement sweeping robot called Panthera, which can adjust its frame width to ease the cleaning tasks to become friendly with different pavement geometry. The expansion and compression operations of the Panthera width are implemented by rotating one high torque motor connecting with the lead screw rod to change the opening angle of linkage hinges. The Panthera cleaning and locomotion operations are synchronized with changing the robot width according to the output of detected pavement width. To this end, the segmented pavement leveraged on the masked based deep convolutional neural network (DCNN) is used as input for the proposed closed-loop feedback control method, enabling the robot to adjust the requirement of changing the width during locomotion accurately. The proposed PID scheme takes into account the robot kinematic design with the flexibility of width changing modes. The experiments were carried out in real environments demonstrated the autonomous reconfiguration robot width with various locomotion scenarios on pavements of varying width.

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Performance Analysis of Parallel and Scalable GPU Based Convolutional Neural Network

Chavan¹,

Kulkarni²

2019

Advances in Intelligent Systems and Computing

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A CUDA-based implementation of convolutional neural network

Cited by 4 publications

References 3 publications

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Reconfiguration During Locomotion by Pavement Sweeping Robot With Feedback Control From Vision System

Performance Analysis of Parallel and Scalable GPU Based Convolutional Neural Network

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