  In recent years, deep learning has become more and more mature, and as a
commonly used algorithm in deep learning, convolutional neural networks have
been widely used in various visual tasks. In the past, research based on deep
learning algorithms mainly relied on hardware such as GPUs and CPUs. However,
with the increasing development of FPGAs, both field programmable logic gate
arrays, it has become the main implementation hardware platform that combines
various neural network deep learning algorithms This article is a review
article, which mainly introduces the related theories and algorithms of
convolution. It summarizes the application scenarios of several existing FPGA
technologies based on convolutional neural networks, and mainly introduces the
application of accelerators. At the same time, it summarizes some accelerators'
under-utilization of logic resources or under-utilization of memory bandwidth,
so that they can't get the best performance.
