Abstract Although existing image-text retrieval methods show strong retrieval performance, they generally fail to explicitly model the subtle semantic difference within categories. To address this problem, we propose a new image-text retrieval method, called CLIP-based Semantic Refinement Method For Image-Text Retrieval (CLIP2SRITR). The proposed method consists of a modal alignment part and a semantic matching part, where the former is to learn better alignment between two modalities and the latter is to enhance the aggregation of image-text features intra-class. The pipeline helps model the distinction between image-text pairs with semantic information differences. In addition, CLIP2SRITR leverages additive angular margin for image-text retrieval loss function to maximize classification boundaries, achieving the goal of increasing intra-class similarity and inter-class variability. Through extensive experiments on three image-text benchmarks, e.g. Wikipedia, Pascal-Sentence, and NUS-WIDE, we show that CLIP2SRITR outperforms state-of-the-art methods, verifying the effectiveness of our method.

With the growing number of data-intensive workloads, GPU, which is the state-of-the-art single-instruction-multiple-thread (SIMT) processor, is hindered by the memory bandwidth wall. To alleviate this bottleneck, previously proposed 3D-stacking near-bank computing accelerators benefit from abundant bank-internal bandwidth by bringing computations closer to the DRAM banks. However, these accelerators are specialized for certain application domains with simple architecture data paths and customized software mapping schemes. For general-purpose scenarios, lightweight hardware designs for diverse data paths, architectural supports for the SIMT programming model, and end-to-end software optimizations remain challenging. To address these issues, we propose Memory-centric Processing Unit (MPU), the first SIMT processor based on 3D-stacking near-bank computing architecture. First, to realize diverse data paths with small overheads, MPU adopts a hybrid pipeline with the capability of offloading instructions to near-bank compute-logic. Second, we explore two architectural supports for the SIMT programming model, including a near-bank shared memory design and a multiple activated row-buffers enhancement. Third, we present an end-to-end compilation flow for MPU to support CUDA programs. To fully utilize MPU’s hybrid pipeline, we develop a backend optimization for the instruction offloading decision. The evaluation results of MPU demonstrate 3.46× speedup and 2.57× energy reduction compared with an NVIDIA Tesla V100 GPU on a set of representative data-intensive workloads.

The traditional model for wind turbine fault prediction is not sensitive to the time sequence data and cannot mine the deep connection between the time series data, resulting in poor generalization ability of the model. To solve this problem, this paper proposes an attention mechanism‐based CNN‐LSTM model. The semantic sensor data annotated by SSN ontology is used as input data. Firstly, CNN extracts features to get high‐level feature representation from input data. Then, the latent time sequence connection of features in different time periods is learned by LSTM. Finally, the output of LSTM is input into the attention mechanism module to obtain more fault‐related target information, which improves the efficiency, accuracy, and generalization ability of the model. In addition, in the data preprocessing stage, the random forest algorithm analyzes the feature correlation degree of the data to get the features of high correlation degree with the wind turbine fault, which further improves the efficiency, accuracy, and generalization ability of the model. The model is validated on the icing fault dataset of No. 21 wind turbine and the yaw dataset of No. 4 wind turbine. The experimental results show that the proposed model has better efficiency, accuracy, and generalization ability than RNN, LSTM, and XGBoost.