FPGA Inroads to Convolutional Neural Networks

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Partitioning FPGA-Optimized Systolic Arrays - GitHub Pages

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  • Using Xilinx FPGA on VMware vSphere for High-throughput
  • Optimizing FPGA-based convolutional neural networks
  • FPGA hardware for each CNN model, signi cant e orts and expertise are required leading to long development time, which makes it di cult to catch up with the rapid development of CNN algorithms. In this work, we present an RTL-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of
  • FPGA et al. Introduction Motivation Uniformed CNN Representation Ca eine Design Roo ine Model Experiment and Result Conclusion Experiment and Result Comparison with CPU/GPU Platforms CPU CPU+GPU CPU+FPGA Device E5-2609 K40 KU60 VX690T Technology 22nm 28nm 20nm 28nm Freq. 1.9GHz 1GHz 200MHz 150MHz Power(W) 150 250 25 26 Latency (ms/image

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Hardware Acceleration of Deep Convolutional Neural

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Memory-Ef?cient Data?ow Inference for Deep CNNs on FPGA

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  • A much more aggressive approach based on FPGA clusters for CNN training has been proposed in [9] , and 15 FPGAs, scaling up to 83 FPGAs at most, are used for training AlexNet, VGG-16 and VGG-19
  • Mar 16, 2017
  • Jul 17, 2018
  • The proposed CNN acceleration scheme and architecture are demonstrated on a standalone Altera Arria 10 GX 1150 FPGA by implementing end-to-end VGG-16 CNN model and achieved 645.25 GOPS of

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Accelerating Binarized Convolutional Neural - GitHub Pages

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2??CNN on FPGA?GPU????????(???)

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  • PipeCNN on Intel® FPGA: Acceleration of Machine Learning
  • Fixed-Point Convolutional Neural Network for Real- Time
  • Aug 29, 2018
  • Jan 14, 2019

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FPGA-Based CNN Inference Accelerator Synthesized from

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FPGA-based Real-Time Super-Resolution - GitHub Pages

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  • celerating CNN inference on FPGAs. Systolic-CNN is highly scal-able and parameterized, which can be easily adapted by users to achieve up to 100% utilization of the coarse-grained computation resources (i.e., DSP blocks) for a given FPGA. Systolic-CNN is also run-time-flexible in the context of multi-tenancy cloud/edge com-
  • Improving the Performance of OpenCL-based FPGA Accelerator
  • Papers with Code - ZynqNet: An FPGA-Accelerated Embedded
  • 1. An efficient 3D CNN (E3DNet): better than standard 3D CNNs (C3D) –37 times smaller –5% more accurate on UCF101 2. An FPGA-based architecture (F-E3D) –high performance and enhanced hardware efficiency 3. Comprehensive comparison –with other 3D CNN models on various platforms Contributions 4

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