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Convolutional neural networks(CNNs) have been widely applied in various applications. However, the computation-intensive convolutional layers and memory-intensive fully connected layers have brought many challenges to the implementation of CNN on embedded platforms. To overcome this problem, this work proposes a power-efficient accelerator for CNNs, and different methods are applied to optimize the...
Today, artificial neural networks (ANNs) are widely used in a variety of applications, including speech recognition, face detection, disease diagnosis, etc. And as the emerging field of ANNs, Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) which contains complex computational logic. To achieve high accuracy, researchers always build large-scale LSTM networks which are time-consuming...
Recently, FPGAs have been widely used in the implementation of hardware accelerators for Convolutional Neural Networks (CNN), especially on mobile and embedded devices. However, most of these existing accelerators are designed with the same concept as their ASIC counterparts, that is all operations from different CNN layers are mapped to the same hardware units and work in a multiplexed way. Although...
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