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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...
Large-scale graphs processing attracts more and more attentions, and it has been widely applied in many application domains. FPGA is a promising platform to implement graph processing algorithms with high power-efficiency and parallelism. In this paper, we propose OmniGraph, a scalable hardware accelerator for graph processing. OmniGraph can process graphs with different sizes adaptively and is adaptable...
The Intrusion Detection Systems (IDS) is becoming important and quite timing/space consuming due to the increasing volume of explosive data flood. During the past decades, there have been plenty of studies proposing software mechanisms to exploit the temporal locality in the IDS systems. However, it requires considerable memory blocks to store the redundancy table, therefore, the performance as well...
As a traditional algorithm, the string match meets a challenge with the development of the massive volume of data be-cause of gene sequencing. Surveys show that there will be a huge amount of short read segments during the process of gene sequencing and the need for a highly efficient is urgent. The BWA is an effective algorithm to deal with the short read mapping. Compared with other short read mapping...
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...
Neural networks have been widely used in a large range of domains, researchers tune numbers of layrs, neurons and synapses to adapt various applications. As a consequence, computations and memory of neural networks models are both intensive. As large requirements of memory and computing resources, it is difficult to deploy neural networks on resource-limited platforms. Sparse neural networks, which...
As the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses significant challenge to construct a high performance implementations of deep learning neural networks. In order to improve the performance as well as to...
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