The Convolutional Neural Network (CNN) has been used in many fields and has achieved remarkable results, such as image classification, face detection, and speech recognition. Compared to GPU (graphics processing unit) and ASIC, a FPGA (field programmable gate array)-based CNN accelerator has great advantages due to its low power consumption and reconfigurable property. However, FPGA’s extremely limited resources and CNN’s huge amount of parameters and computational complexity pose great challenges to the design. Based on the ZYNQ heterogeneous platform and the coordination of resource and bandwidth issues with the roofline model, the CNN accelerator we designed can accelerate both standard convolution and depthwise separable convolution with a high hardware resource rate. The accelerator can handle network layers of different scales through parameter configuration and maximizes bandwidth and achieves full pipelined by using a data stream interface and ping-pong on-chip cache. The experimental results show that the accelerator designed in this paper can achieve 17.11GOPS for 32bit floating point when it can also accelerate depthwise separable convolution, which has obvious advantages compared with other designs.

Gene function prediction is used to assign biological or biochemical functions to genes, which continues to be a challenging problem in modern biology. Genes may exhibit many functions simultaneously, and these functions are organized into a hierarchy, such as a directed acyclic graph (DAG) for Gene Ontology (GO). Because of these characteristics, gene function prediction can be seen as a typical hierarchical multi-label classification (HMC) task. A novel HMC method based on neural networks is proposed in this article for predicting gene function based on GO. The proposed method belongs to a local approach by transferring the HMC task to a set of subtasks. There are three strategies implemented in this method to improve its performance. First, to tackle the imbalanced data set problem when building the training data set for each class, negative instances selecting policy and SMOTE approach are used to preprocess each imbalanced training data set. Second, a particular multi-layer perceptron (MLP) is designed for each node in GO. Third, a post processing method based on the Bayesian network is used to guarantee that the results are consistent with the hierarchy constraint. The experimental results indicate that the proposed HMC-MLPN method is a promising method for gene function prediction based on a comparison with two other state-of-the-art methods.

Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms.