Abstract
In order to simplify the hardware design and reduce the resource requirements, this paper proposes a novel implementation of a convolutional auto-encoder (CAE) in a field programmable gate array (FPGA). Instead of the traditional framework realized in a layer-by-layer way, we designed a new periodic layer-multiplexing framework for CAE. Only one layer is introduced and periodically reused to establish the network, which consumes fewer hardware resources. Moreover, by fixing the number of channels, this framework can be applicable to an image of arbitrary size. Furthermore, to effectively improve the speed of convolution calculation, the parallel convolution method is used based on shift registers. Experimental results show that the proposed CAE framework achieves good performance in image compression. It can be observed that our CAE framework has advantages in resources occupation, operation speed, and power consumption, indicating great potential for application in digital signal processing.
Highlights
Convolutional auto-encoder (CAE), a kind of convolutional neural network (CNN), adopts an unsupervised learning algorithm for encoding [1,2,3,4,5]
As the experimental results show, in terms of compression speed, it consumes 15.73 ms running on field programmable gate array (FPGA) and 115.29 ms running on CPU
An implementation of CAE based on FPGA is presented in this paper, which newly introduces a periodic layer-multiplexing framework
Summary
Convolutional auto-encoder (CAE), a kind of convolutional neural network (CNN), adopts an unsupervised learning algorithm for encoding [1,2,3,4,5]. Rumelhart first proposed the concept of an auto-encoder [6] and employed it to process data with large dimensions, which promoted the development of neural networks. In 2006, Hinton et al [7] improved the original shallow auto-encoder and proposed the concept of a deep learning neural network as well as its training strategy, which can be used in the signal processing field for applications such as feature extraction [8], image compression [9,10,11], classification [12,13], image denoising [14], prediction [15], and so on. In [17], a three-layer multilayer perceptron structure was presented for image compression, which verified the applicability of the neural network in the field of image compression
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