A high performance FPGA-based accelerator for large-scale convolutional neural networks

In recent years, convolutional neural networks (CNNs) based machine learning algorithms have been widely applied in computer vision applications. However, for large-scale CNNs, the computation-intensive, memory-intensive and resource-consuming features have brought many challenges to CNN implementat...

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Bibliographic Details
Published in:International Conference on Field-programmable Logic and Applications pp. 1 - 9
Main Authors: Huimin Li, Xitian Fan, Li Jiao, Wei Cao, Xuegong Zhou, Lingli Wang
Format: Conference Proceeding
Language:English
Published: EPFL 01.08.2016
Subjects:
AlexNet
Bandwidth
Computational modeling
Convolution
convolutional neural networks
memory bandwidth
Neurons
Parallel processing
parallelism
pipeline
System-on-chip
Throughput
ISSN:1946-1488
Online Access:Get full text
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Summary:In recent years, convolutional neural networks (CNNs) based machine learning algorithms have been widely applied in computer vision applications. However, for large-scale CNNs, the computation-intensive, memory-intensive and resource-consuming features have brought many challenges to CNN implementations. This work proposes an end-to-end FPGA-based CNN accelerator with all the layers mapped on one chip so that different layers can work concurrently in a pipelined structure to increase the throughput. A methodology which can find the optimized parallelism strategy for each layer is proposed to achieve high throughput and high resource utilization. In addition, a batch-based computing method is implemented and applied on fully connected layers (FC layers) to increase the memory bandwidth utilization due to the memory-intensive feature. Further, by applying two different computing patterns on FC layers, the required on-chip buffers can be reduced significantly. As a case study, a state-of-the-art large-scale CNN, AlexNet, is implemented on Xilinx VC709. It can achieve a peak performance of 565.94 GOP/s and 391 FPS under 156MHz clock frequency which outperforms previous approaches.
ISSN:1946-1488
DOI:10.1109/FPL.2016.7577308