As memory-intensive applications continue to drive the need for advanced architectural solutions, Compute Express Link (CXL) has risen as a promising interconnect technology that enables seamless high-speed, low-latency communication between host processors and various peripheral devices. In this study, we explore the application performance of ASIC CXL memory in various data-center scenarios. We then further explore multiple potential impacts (e.g., throughput, latency, and cost reduction) of employing CXL memory via carefully designed policies and strategies. Our empirical results show the high potential of CXL memory, reveal multiple intriguing observations of CXL memory and contribute to the wide adoption of CXL memory in real-world deployment environments. Based on our benchmarks, we also develop an Abstract Cost Model that can estimate the cost benefit from using CXL memory.

A practical hyperspectral target characterization task estimates a target signature from imprecisely labeled training data. The imprecisions arise from the characteristics of the real-world tasks. First, accurate pixel-level labels on training data are often unavailable. Second, the subpixel targets and occluded targets cause the training samples to contain mixed data and multiple target types. To address these imprecisions, this paper proposes a new hyperspectral target characterization method to produce diverse multiple hyperspectral target signatures under a multiple instance learning (MIL) framework. The proposed method uses only bag-level training samples and labels, which solves the problems arising from the mixed data and lack of pixel-level labels. Moreover, by formulating a multiple characterization MIL and including a diversity-promoting term, the proposed method can learn a set of diverse target signatures, which solves the problems arising from multiple target types in training samples. The experiments on hyperspectral target detections using the learned multiple target signatures over synthetic and real-world data show the effectiveness of the proposed method.

Recently, deep models have shown powerful ability for remote sensing scene representation. However, the training process of these deep methods requires large amount of labelled samples while usual remote sensing image datasets cannot provide enough training samples. Therefore, the learned model is usually suboptimal. To solve the problem, this work focuses on obtaining multiple choices by training multiple models simultaneously, and then the human oracle can choose a proper one from these choices. However, training several models separately usually makes the obtained results similar. This paper tries to diversify the obtained choices by encouraging the obtained choices to repulse from each other. Experiments are conducted on Ucmerced Land Use dataset to validate the effectiveness of the proposed method to provide multiple diversified choices.