Systems, Apparatuses, and Methods for K Nearest Neighbor Search

    公开(公告)号:US20170139948A1

    公开(公告)日:2017-05-18

    申请号:US14944828

    申请日:2015-11-18

    CPC classification number: G06F17/10 G06F17/30979 G06K9/00986 G06K9/6276

    Abstract: Systems, apparatuses, and methods for k-nearest neighbor (KNN) searches are described. In particular, embodiments of a KNN accelerator and its uses are described. In some embodiments, the KNN accelerator includes a plurality of vector partial distance computation circuits each to calculate a partial sum, a minimum sort network to sort partial sums from the plurality of vector partial distance computation circuits to find k nearest neighbor matches and a global control circuit to control aspects of operations of the plurality of vector partial distance computation circuits.

    ADAPTIVE SPECULATIVE DECODING
    6.
    发明申请

    公开(公告)号:US20200036389A1

    公开(公告)日:2020-01-30

    申请号:US16592465

    申请日:2019-10-03

    Abstract: Examples herein relate to decoding tokens using speculative decoding operations to decode tokens at an offset from a token decoded by a sequential decoding operation. At a checkpoint, a determination is made as to whether tokens to be decoded by the sequential and speculative decoding operations align. If there is alignment, the speculatively decoded tokens after a discard window are committed and made available for access. If there is not alignment, the speculatively decoded tokens are discarded. A miss in alignment and a fullness level of a buffer that stores speculatively decoded tokens are assessed to determine a next offset level for a start of speculative decoding. A size of a discard window can be set using a relationship based on the offset level to improve buffer utilization and to attempt to improve changes of alignments.

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