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FPGAs, characterized by their low power consumption and swift response, are ideally suited for parallel computations associated with object detection tasks, making them a popular choice for target detection and neural network acceleration. ...

• Parallel computation boosts speed and efficiency in convolutional layers.
• Integrated parallel processing enhances convolution activation pooling.
• Efficient memory management reduces read/write time for feature layers.
• ...

The purpose of this article is to present a system that enhances the security, efficiency, and reconfigurability of an Internet-of-Things (IoT) system used for surveillance and monitoring. A Multi-Processor System-On-Chip (MPSoC) composed of ...

The novel communication 6G requires raw data rates of up to 400 Gbit s− 1 in a single Field Programmable Gate Array (FPGA) front-end. For these high data rates, a Software Defined Radio (SDR) on a multi-core processor reaches a performance limit due to ...

This paper presents a configurable convolutional neural network accelerator (CNNA) for a system-on-chip (SoC). The goal was to accelerate inference in different deep learning networks on an embedded SoC platform. The presented CNNA has ...

• A scalable CNN architecture with a single computation engine for FPGA acceleration.

With the rapid development of deep learning, traditional convolutional neural networks have achieved success in various fields. In order to meet the situation that FPGAs and other devices have less available resources, a picture classification system ...

Convolutional Neural Network (CNN) has been widely used in computer vision fields such as image recognition and target detection. However, in the forward reasoning stage, many practical applications often require features of low latency and low power ...

In a quest for making FPGA technology more accessible to the software community, Xilinx recently released PYNQ, a framework for Zynq that relies on Python and overlays to ease the integration of functionalities of the programmable logic into ...

In the past few years, Convolutional Neural Networks (CNNs) have seen a massive improvement, outperforming other visual recognition algorithms. Since they are playing an increasingly important role in fields such as face recognition, augmented reality or ...

Using field-programmable gate arrays (FPGAs) as a substrate to deploy soft graphics processing units (GPUs) would enable offering the FPGA compute power in a very flexible GPU-like tool flow. Application-specific adaptations like selective hardening of ...