A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

2018/12/25 by Teng Wang, Wang, Teng, Chao Wang +5 · 1 voice · 41 citations
Computer Science · Engineering · #Advanced Memory and Neural Computing #Advanced Neural Network Applications #Artificial intelligence #Artificial neural network #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #Computer architecture #Computer engineering #Computer science #Deep learning #Distributed #Embedded system #FOS: Computer and information sciences #Field-programmable gate array #Machine learning #Operating system #Parallel #Software #Template #and Cluster Computing (cs.DC) #cs.CV #cs.DC

paper · pdf · doi:10.48550/arxiv.1901.04988

published in arXiv (Cornell University) (Cornell University) · Some part in the section of introduction dont have the labeling reference. And there are some wrong of data in figure

openalex publication_date 2018/12/25 · arxiv published 2018/12/25 · openalex created_date 2019/01/25 · arxiv created 2019/12/25 · arxiv updated 2019/12/30 · openalex updated_date 2026/07/28

Abstract

With the rapid development of in-depth learning, neural network and deep learning algorithms have been widely used in various fields, e.g., image, video and voice processing. However, the neural network model is getting larger and larger, which is expressed in the calculation of model parameters. Although a wealth of existing efforts on GPU platforms currently used by researchers for improving computing performance, dedicated hardware solutions are essential and emerging to provide advantages over pure software solutions. In this paper, we systematically investigate the neural network accelerator based on FPGA. Specifically, we respectively review the accelerators designed for specific problems, specific algorithms, algorithm features, and general templates. We also compared the design and implementation of the accelerator based on FPGA under different devices and network models and compared it with the versions of CPU and GPU. Finally, we present to discuss the advantages and disadvantages of accelerators on FPGA platforms and to further explore the opportunities for future research.

Citations

• Recurrent Neural Network Regularization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Fast Learning Algorithm for Deep Belief Nets
• Ternary Weight Networks
• DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
• Trained Ternary Quantization
• A Survey of FPGA-Based Neural Network Accelerator

Cited by

• Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors

Discussions

• Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities [hn, 3 points, 0 comments]

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