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- W2979937837 abstract "Recent advancements in deep learning techniques facilitate intelligent-query support in diverse applications, such as content-based image retrieval and audio texturing. Unlike conventional key-based queries, these intelligent queries lack efficient indexing and require complex compute operations for feature matching. To achieve high-performance intelligent querying against massive datasets, modern computing systems employ GPUs in-conjunction with solid-state drives (SSDs) for fast data access and parallel data processing. However, our characterization with various intelligent-query workloads developed with deep neural networks (DNNs), shows that the storage I/O bandwidth is still the major bottleneck that contributes 56%--90% of the query execution time." @default.
- W2979937837 created "2019-10-18" @default.
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- W2979937837 date "2019-10-12" @default.
- W2979937837 modified "2023-10-18" @default.
- W2979937837 title "DeepStore" @default.
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- W2979937837 doi "https://doi.org/10.1145/3352460.3358320" @default.
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