Abstract Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. Many applications demand for embedded s

2021-02-26

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 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 CNN is a highly efficient CNN topology.

Zynqnet

ZynqNet CNN. David Gschwend (see the master thesis repository) YOLO. Joseph Redmon, Ali Farhadi. SqueezeNet. Forrest Iandola, Matthew Moskewicz, Khalid Ashraf, Song ZynqNet CNN. David Gschwend (see the master thesis repository) SqueezeNet. Forrest Iandola, Matthew Moskewicz, Khalid Ashraf, Song Han, William Dally, Kurt Keutzer. ZynqNet accelerates not just the convolutional layers of SqueezeNet but also the ReLU nonlinearities, concatenation, and the global average pooling layers on the Zynqbox, which includes a Xilinx Zynq XC-7Z045 SoC, 1 GB DDR3 memory for the ARM processor, 768MB independent DDR3 memory for the programmable logic (PL), and a 1 GHz CPU is connected to the PL via AXI4 ports for data transfer. accuracy [6].

This repository contains the results from my Master Thesis. Report.

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.

The FPGA accelerator has been synthesized using High-Level Synthesis for the Xilinx Zynq XC-7Z045, Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. 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 ZynqNet: A FPGA-Accelerated Embedded Convolutional Neural Network.

accuracy [6]. 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. However, this chip had many more resources needed compared to us. CNN2ECST, was designed by an Italian group, and similar to our goal.

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SqueezeNet is modified to be made more "FPGA friendly", and later a general accelerator is designed using HLS. The Zynqnet project report. Topic: ZynqNet – FPGA-Accelerated CNN ○ https://github.com/ dgschwend/zynqnet ○ https://github.com/pp 14 Oct 2016 Gitlab service will be suspended from Friday 12th at 19:30 until Friday 12th at 21: 00.
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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 at a computational complexity of only 530 million multiplyaccumulate Nunez-Prieto, R, Gomez, PC & Liu, L 2019, A Real-Time Gesture Recognition System with FPGA Accelerated ZynqNet Classification. in J Nurmi, P Ellervee, K Halonen & J Roning (eds), 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 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. 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 Figure C.1.: 3D Illustration of the Convolutional Layers in a SqueezeNet or ZynqNet Fire Module.

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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Master Thesis "ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network" - CharlesXu/zynqnet This course will teach you how to build

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The ZynqNet Embedded CNN is designed for image classification on ImageNet and consists of ZynqNet CNN, an optimized and customized CNN topology, and

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