Institutionen för elektro- och informationsteknik
Elektro- och informationsteknik, Utbildning, Examensarbeten
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. 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.
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2021-04-08 · 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). 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. 2020-05-14 · 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.
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Institutionen för elektro- och informationsteknik
ZynqNet zynqnet_report.pdf Netscope Visualization Tool for Convolutional Neural Networks. Network Analysis Article. Impact of Single Event Upsets on Convolutional Neural Networks in Xilinx Zynq FPGA. February 2021; IEEE Transactions on Nuclear Science PP(99):1-1 We present CNN-Grinder, a template-driven workflow for converting algorithmic descriptions of mobile-friendly convolutional neural networks (CNNs), such as SqueezeNet v1.1 and ZynqNet, to HLS code which can be used for programming low-end-low-cost FPGA SoCs.
Department of Electrical and Information Technology
, indata.bin, weights.bin, unittests.
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∙ 0 ∙ share Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. 2020-05-14 · ZynqNet CNN is a highly efficient CNN topology.
Institutionen för elektro- och informationsteknik
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. This repository contains the results from my Master Thesis. Report.
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. 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.