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Nunez-Prieto, R, Gomez, PC & Liu, L 2019, A Real-Time Gesture Recognition System with FPGA Accelerated ZynqNet Classification. i J Nurmi, P Ellervee, K Halonen & J Roning (red), 2019 IEEE Nordic Circuits and Systems Conference, NORCAS 2019: NORCHIP and International Symposium of System-on-Chip, SoC 2019 - Proceedings., 8906956, Institute of Electrical and Electronics Engineers Inc., 5th IEEE

2019-02-08 · This work presents a workflow for deep learning mobile application acceleration on small low-cost low-power FPGA devices using HLS tools. This workflow eases the design of an improved version of the SqueezeJet accelerator used for the speedup of mobile-friendly low-parameter ImageNet class CNNs, such as the SqueezeNet v1.1 and the ZynqNet. 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. (embedded systems’ friendly) Zynqnet CNN topology has been modiﬁed to ﬁt the application. All together allow more than 85% of the images to be successfully identiﬁed using a regular GPU training system.

A web-based tool for visualizing and analyzing convolutional neural network architectures (or … The ZynqNet FPGA Accelerator allows an efficient evaluation of ZynqNet CNN. It accelerates the full network based on a nested-loop algorithm which minimizes the number of arithmetic operations and memory accesses. The FPGA accelerator has been synthesized using High-Level Synthesis for the Xilinx Zynq XC-7Z045, 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 architecture for its evaluation. ZynqNet CNN is a highly efficient CNN topology. 背景：ZynqNet能在xilinx的FPGA上实现deep compression。目的：读懂zynqNet的代码和论文。目录一、网络所需的运算与存储1.1 运算操作:1.2 Memory requirements:1.3 需求分析：1.4 FPGA based accelerator需要执行：二、网络结构针对网络结构进行了三种优化： FPGA-real 2020-03-01 Mentor Graphics Cairo University ONE Lab Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

14 May 2020 ZynqNet CNN is a highly efficient CNN topology. Detailed analysis and optimization of prior topologies using the custom-designed Netscope

One of its major components is the fire layer. Fire layers start out with a "squeeze" step (a few 1x1 convolutions) and lead to two "expand" steps, which include a 1x1 and a 3x3 convolution followed by concatenation of the two results. 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 architecture for its evaluation.

Development and project management platform. Switch branch/tag. ZynqNet zynqnet_report.pdf

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.

[转载]【计算机科学】【2016.08】【含源码】ZynqNet：一种FPGA加速的嵌入式卷积神经网络. 已有1132 次阅读 2019-11-16 18:38 |系统分类:科研笔记|文章来源: 14 May 2020 ZynqNet CNN is a highly efficient CNN topology.
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ZynqNet CNN. David Gschwend (see the master thesis repository) YOLO.

SqueezeNet is modified to be made more "FPGA friendly", and later a general accelerator is designed using HLS. The Zynqnet project report.
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14 May 2020 ZynqNet CNN is a highly efficient CNN topology. Detailed analysis and optimization of prior topologies using the custom-designed Netscope

CNN2ECST, was designed by an Italian group, and similar to our goal. ZynqNet derived from SqueezeNet by replacing the combination of convolutional and maxpool layers with a convolutional layer having increased stride . This transformation simplifies the accelerator design; by implementing a convolutional layer and a global pooling layer, the ZynqNet accelerator can process the whole CNN except the last softmax layer. Development and project management platform.

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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

The ZynqNet FPGA Accelerator, a specialized FPGA architecture for the efficient acceleration of ZynqNet CNN and similar convolutional neural networks. ZynqNet CNN is trained offline on GPUs using the Caffe framework, while the ZynqNet FPGA Accelerator employs the CNN for image classification, or inference , on a Xilinx Zynq XC- 7Z045 System-on-Chip (SoC).

Implementation of an 8-bit Dynamic Fixed-Point Convolutional Neural Network for Human Sign Language Recognition on a Xilinx FPGA Board RICARDO NÚÑEZ PRIETO Article. Impact of Single Event Upsets on Convolutional Neural Networks in Xilinx Zynq FPGA.

Netscope CNN Analyzer. A web-based tool for visualizing and analyzing convolutional neural network architectures (or technically, any directed acyclic graph).