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TRIM is a recommended command to deliver data invalidation information of the file system to flash storage. It is issued on both system level and device level. Since it can reduce the number of data copies during device-level garbage collection (DGC), TRIM has been widely used to improve the endurance and performance of mobile devices. Contrary to the common belief, this work identifies that the default TRIM scheme has both merit and drawback to the performance of mobile devices, especially in flash-friendly file system (F2FS), which is a commonly used file system in mobile devices. On one hand, TRIM can reduce garbage collection migration to prolong the flash lifetime as well as improving I/O throughput; On the other hand, TRIM may induce I/O contentions. This article proposes a new TRIM scheme, iTRIM, to distribute the timing overheads to system idle time. To further reduce I/O contention and improve I/O performance, the design of iTRIM considers the TRIM size, and the logical addresses’ pattern of victim invalidated data. Experimental results show that iTRIM can minimize I/O contentions while retaining the benefits of the default TRIM scheme for endurance and performance.

While the computing power of mobile devices has been quickly evolving in recent years, the growth of mobile storage capacity is, however, relatively slower. A common problem shared by budget-phone users is that they frequently run out of storage space. This article conducts a deep inspection of file usage of mobile applications and their potential implications on user experience. Our major findings are as follows: First, mobile applications could rapidly consume storage space by creating temporary cache files, but these cache files quickly become obsolete after being re-used for a short period of time. Second, file access patterns of large files, especially executable files, appear highly sparse and random, and therefore large portions of file space are never visited. Third, file prefetching brings an excessive amount of file data into page cache but only a few prefetched data are actually used. The unnecessary memory pressure causes premature memory reclamation and prolongs application launching time. Through the feasibility study of two preliminary optimizations, we demonstrated a high potential to eliminate unnecessary storage and memory space consumption with a minimal impact on user experience.

Read-ahead schemes of page cache have been widely used to improve read performance of Linux systems. As Android system inherits the Linux kernel, traditional read-ahead schemes are directly applied in mobile devices. However, read request sizes and page cache sizes in mobile devices are much smaller than other platforms, which may decrease read-ahead efficiency and hurt user experience. The read-ahead efficiency is defined as hit pages / all pre-fetched pages in a sequential read. To study the efficiency of traditional read-ahead in mobile devices, this paper first observes that many pre-fetched pages are unused in page cache, which causes high page cache eviction ratio with high extra access latency. Then, this paper analyzes the factors that closely relate to the access latency. It is found that there exists a trade-off between read-ahead size and access latency. A size-tuning scheme is then proposed to explore this trade-off. Experimental results on real mobile devices have shown that the proposed scheme can reduce the number of pre-fetched pages and improve the efficiency of read-ahead without decreasing the page cache hit ratio.

Mobile devices, such as smartphones, have become a necessity in our daily life. However, users may notice that after being used for a long time, mobile devices begin to exhibit a sluggish response. Based on an empirical study on a collection of aged smartphones, this work identified that file fragmentation is among the key factors that contribute to the progressive degradation of response time. This study takes a three-step approach: First, this study designed a set of reproducible file-system aging processes based on User-Interface (UI) script replay. Through the aging processes, it confirmed that file fragmentation quickly emerged, and SQLite files were among the most severely fragmented files. Second, based on the workloads of a selection of popular mobile applications, this study observed that file fragmentation did have an impact on user-perceived latencies. Specifically, the launching time of Chrome on an aged file system was 79 percent slower than it was on a pristine file system. Third, this study evaluated existing treatments of file fragmentation, including space preallocation, persistent journal, and file defragmentation to understand their efficacies and limitations. This study also evaluated a state-of-the-art copyless defragmenter, $\mathsf{janusd}$janusd, to show its advantage over the existing methods.

In this paper, we propose the hybrid deep neural network-based cross-modal image and text retrieval method to explore complex cross-modal correlation by considering multi-layer learning. First, we propose intra-modal and inter-modal representations to achieve a complementary single-modal representation that preserves the correlation between the modalities. Second, we build an association between different modalities through hierarchical learning to further mine the fine-grained latent semantic association among multimodal data. The experimental results show that our algorithm substantially enhances retrieval performance and consistently outperforms four comparison methods.

Sea-land segmentation based on edge detection is commonly utilized in ship detection, coastline extraction, and satellite system applications due to its high accuracy and rapid speed. Pixel-level distribution statistics do not currently satisfy the requirements for high-resolution, large-scale remote sensing image processing. To address the above problem, in this paper, we propose a high-throughput hardware architecture for sea-land segmentation based on multi-dimensional parallel characteristics. The proposed architecture is well suited to wide remote sensing images. Efficient multi-dimensional block level statistics allow for relatively infrequent pixel-level memory access; a boundary block tracking process replaces the whole-image scanning process, markedly enhancing efficiency. The tracking efficiency is further improved by a convenient two-step scanning strategy that feeds back the path state in a timely manner for a large number of blocks in the same direction appearing in the algorithm. The proposed architecture was deployed on Xilinx Virtex k7-410t to find that its practical processing time for a [Formula: see text] remote sensing image is only about 0.4[Formula: see text]s. The peak performance is 1.625[Formula: see text]gbps, which is higher than other FPGA implementations of segmentation algorithms. The proposed structure is highly competitive in processing wide remote sensing images.

The increasing density and capacity of NAND flash memory leads to degraded reliability. To address the reliability issue, low-density parity-check code (LDPC) has been deployed in NAND flash memories due to its strong error correction capability. The drawback of LDPC is that, to correct data with high raw bit error rate (RBER), read latency will be amplified. To improve read performance, this paper proposes to apply lossless compression to reduce RBER on data pages. However, compression and decompression incur time overheads. Compressing all the data pages for RBER reduction will degrade write performance. In addition, the variation of compression ratio leads to variation of RBER reduction, thus varied read latency reduction. In this work, a selective data compression scheme is proposed for read performance improvement. Both read frequency and compression ratio of data are taken into consideration. Data in a flash page with high read frequency and good compressibility are prioritized for compression. Experimental results show that the proposed scheme can improve read performance by 42% on average, without impacting write performance.