Bi-scaled deep neural networks
    2.
    发明授权

    公开(公告)号:US11263518B2

    公开(公告)日:2022-03-01

    申请号:US16593284

    申请日:2019-10-04

    Abstract: A method is provided for forming a Deep Neural Network (DNN). The method includes quantizing deep learning data structures of the DNN into at least two modes using at least two scale factors, respectively. Each of the at least two modes corresponds to a respective one of the at least two scale factors. The method further includes identifying which of the at least two scale factors to use for a given one of the data structures based on a data distribution of the given one of the data structures. The quantizing step includes identifying when a tail of the given one of the data structures starts by (i) building a histogram of values in the given one of the data structures using successive bins; (ii) identifying a ratio of density between the successive bins; and (iii) checking whether the ratio of density is greater than a ratio of density threshold.

    PREFETCH THRESHOLD FOR CACHE RESTORATION
    6.
    发明申请
    PREFETCH THRESHOLD FOR CACHE RESTORATION 有权
    用于缓存恢复的预设阈值

    公开(公告)号:US20160357676A1

    公开(公告)日:2016-12-08

    申请号:US14733168

    申请日:2015-06-08

    CPC classification number: G06F12/0862 G06F9/48 G06F9/4887 G06F2212/602

    Abstract: Embodiments relate to a prefetch threshold for cache restoration. An aspect includes determining, based on a task switch from an outgoing task to a current task in a processor, a prefetch threshold for a next task, the prefetch threshold corresponding to an expected runtime of the current task and an amount of time required to prefetch data for the next task. Another aspect includes starting prefetching for the next task while the current task is executing based on the prefetch threshold.

    Abstract translation: 实施例涉及用于高速缓存恢复的预取阈值。 一方面包括基于从出局任务到处理器中的当前任务的任务切换来确定下一任务的预取阈值,对应于当前任务的预期运行时间的预取阈值以及预取所需的时间量 下一个任务的数据。 另一方面包括在当前任务基于预取阈值执行时启动下一个任务的预取。

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