Abstract: This paper presents a real-time hand gesture recognition system by accelerating a convolutional neural network (CNN) using FPGA platform. More specifically, ZynqNet is adopted and modified to fulfill the classification task of recognizing the Swedish manual alphabet, which is used by sign language users for spelling purposes, also known as fingerspelling.

Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition and object detection.

The ZynqNet Embedded CNN is designed for image classification on ImageNet and consists of ZynqNet CNN, an optimized and customized CNN topology, and the ZynqNet FPGA Accelerator, an FPGA-based ZynqNet: A FPGA-Accelerated Embedded Convolutional Neural Network. This repository contains the results from my Master Thesis. Report. The report includes. an overview and detailed analysis of many popular CNN architectures for Image Classification (AlexNet, VGG, NiN, GoogLeNet, Inception v.X, ResNet, SqueezeNet) Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles.

Zynqnet

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During Zynqnet development,. SqueezeNet is modified to be made more "FPGA friendly", and later a general accelerator is designed using HLS. The Zynqnet

. . 26 3.2 RelativeenergyandareasavingfactorsbycomparingINT8with Abstract Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles.

4.Type "vivado_hls -p proj_ZynqNet" to open HLS project. Starred 0 Star 0 Fork 1

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More specifically, ZynqNet is adopted and modified to fulfill the classification task of recognizing the Swedish manual alphabet, which is used by sign language users for spelling purposes, also known as fingerspelling. ZynqNet: Modi cation ZynqNet was adapted for a gesture recognition system: • Optimizations to the FPGA Accelerator: • 8-bit xed-point scheme • No o -chip memory usage • Fine-tuning of the NN leads almost the same accuracy • Performance: 23.5 FPS 20 2021-02-26 · Fault injection results show that the TMRed ZynqNet reduces the soft error rate (SER) by 33.59% with a circuit area increase of 111.92% when compared with the standard ZynqNet. The experimental results demonstrate that the quantized ZynqNet reduces the SER by 71.36% with a circuit area reduction of 44.76% when compared with the standard ZynqNet. The network topology of choice is Zynqnet, proposed by Gschwend in 2016, which is a topology that has already been implemented successfully on an FPGA platform and it has been trained with the large picture dataset provided by ImageNet, for its popular image recognition contest. ZynqNet CNN. David Gschwend (see the master thesis repository) YOLO. Joseph Redmon, Ali Farhadi.
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Ricardo Nunez-Prieto, Pablo Correa Gomez & Liang Liu, 2019 Nov 21, A Real-Time Gesture Recognition System with FPGA Accelerated ZynqNet Classification. Ricardo Nunez-Prieto, Pablo Correa Gomez & Liang Liu, 2019 nov 21, ZynqNet CNN is a highly efficient CNN topology. Detailed analysis and optimization of prior topologies using the custom-designed Netscope CNN Analyzer have enabled a CNN with 84.5% top-5 accuracy at a computational complexity of only 530 million multiplyaccumulate operations. ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network Edit social preview 14 May 2020 • David Gschwend ZynqNet CNN is a highly efficient CNN topology.
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The network topology of choice is Zynqnet, proposed by Gschwend in 2016, which is a topology that has already been implemented successfully on an FPGA platform and it has been trained with the large picture dataset provided by ImageNet, for its popular image recognition contest.

The ZynqNet FPGA accelerator had been synthesized using high-level synthesis for the Xilinx Zynq XC-7Z045, reached 200 MHz clock frequency with a device utilization of 80 to 90 percent.