Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data

Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data

In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is t...

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Bibliographic Details
Main Authors: João Silva, Pedro Pereira, Rui Machado, Rafael Névoa, Pedro Melo-Pinto, Duarte Fernandes
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Sensors
Subjects:
convolutional neural network (CNN)
hardware accelerator
field-programmable gate array (FPGA)
light detection and ranging (LiDAR)
quantization
object detection
Online Access:https://www.mdpi.com/1424-8220/22/6/2184

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