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    Memory subsystem operations with unaligned and scatter gather feature to support convolution and dimension shuffle
    1.
    发明授权
    Memory subsystem operations with unaligned and scatter gather feature to support convolution and dimension shuffle 有权

    公开(公告)号：US11132124B2

    公开(公告)日：2021-09-28

    申请号：US16006752

    申请日：2018-06-12

    Applicant: Intel Corporation

    Inventor： Olivia Wu ,  Prashant Arora ,  Jason Ko

    IPC: G06F17/16 ,  G06F3/06 ,  G06N3/08 ,  G06N3/063 ,  G06N3/04 ,  G06T1/60

    Abstract: One embodiment provides an apparatus. The apparatus may include memory circuitry to store tensor data representing a tensor. The apparatus may include memory controller circuitry to access the memory circuitry. The apparatus may include processor circuitry to: receive a request for a tensor operation; generate a plurality of sub-commands for the tensor operation; and provide the sub-commands to memory controller circuitry to perform the tensor operation based on instructions contained in one or more of the sub-commands. The instructions contained in one or more of the sub-commands may include identify addresses in memory to access; activate one or more rows in the memory circuitry that correspond to the addresses; and transfer tensor data to and/or from the memory circuitry.

    UPDATING AN ARTIFICIAL NEURAL NETWORK USING FLEXIBLE FIXED POINT REPRESENTATION
    5.
    发明申请
    UPDATING AN ARTIFICIAL NEURAL NETWORK USING FLEXIBLE FIXED POINT REPRESENTATION 审中-公开
    使用灵活的固定点表示更新人工神经网络

    公开(公告)号：US20170061279A1

    公开(公告)日：2017-03-02

    申请号：US14597091

    申请日：2015-01-14

    Applicant: Intel Corporation

    Inventor： Andrew Yang ,  Carey Kloss ,  Prashant Arora ,  Alex S. Park ,  Naveen G. Rao ,  Amir Khosrowshahi

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/084

    Abstract: Updating an artificial neural network is disclosed. A node characteristic is represented using a fixed point node characteristic parameter. A network characteristic is represented using a fixed point network characteristic parameter. The fixed point node characteristic parameter and the fixed point network characteristic parameter are processed to determine a fixed point intermediate parameter having a larger size than either the fixed point node characteristic parameter or the fixed point network characteristic parameter. A value associated with the fixed point intermediate parameter is truncated according to a system truncation schema. The artificial neural network is updated according to the truncated value.

    Abstract translation: 公开了更新人造神经网络。 使用固定点节点特征参数来表示节点特性。 使用固定点网络特性参数表示网络特性。 处理固定点节点特征参数和固定点网络特征参数，以确定具有比固定点节点特征参数或固定点网络特征参数大的大小的固定点中间参数。 与固定点中间参数相关联的值根据系统截断模式被截断。 人工神经网络根据截断值进行更新。

    MATRIX OPERANDS FOR LINEAR ALGEBRA OPERATIONS
    8.
    发明申请
    MATRIX OPERANDS FOR LINEAR ALGEBRA OPERATIONS 有权
    线性运算的矩阵运算

    公开(公告)号：US20170060811A1

    公开(公告)日：2017-03-02

    申请号：US14697728

    申请日：2015-04-28

    Applicant: Intel Corporation

    Inventor： Andrew Yang ,  Carey Kloss ,  Prashant Arora ,  Tony Werner ,  Naveen Gandham Rao ,  Amir Khosrowshahi

    IPC: G06F17/16 ,  G06N3/08 ,  G06F12/02

    CPC classification number: G06F17/16 ,  G06F12/023 ,  G06F2212/251 ,  G06N3/08

    Abstract: Described herein are methods, systems, and apparatuses to utilize a matrix operation by accessing each of the operation's matrix operands via a respective single memory handle. This use of a single memory handle for each matrix operand eliminates significant overhead in memory allocation, data tracking, and subroutine complexity present in prior art solutions. The result of the matrix operation can also be accessible via a single memory handle identifying the matrix elements of the result.

    Abstract translation: 这里描述了通过经由相应的单个存储器句柄访问每个操作的矩阵操作数来利用矩阵运算的方法，系统和装置。 每个矩阵操作数使用单个存储器句柄消除了现有技术解决方案中存在的内存分配，数据跟踪和子程序复杂度方面的重大开销。 矩阵运算的结果也可以通过识别结果的矩阵元素的单个存储器句柄来访问。

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