HYBRID MACHINE LEARNING ARCHITECTURE WITH NEURAL PROCESSING UNIT AND COMPUTE-IN-MEMORY PROCESSING ELEMENTS

    公开(公告)号:US20230025068A1

    公开(公告)日:2023-01-26

    申请号:US17813834

    申请日:2022-07-20

    Abstract: Methods and apparatus for performing machine learning tasks, and in particular, a hybrid architecture that includes both neural processing unit (NPU) and compute-in-memory (CIM) elements. One example neural-network-processing circuit generally includes a plurality of CIM processing elements (PEs), a plurality of neural processing unit (NPU) PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs. One example method for neural network processing generally includes processing data in a neural-network-processing circuit comprising a plurality of CIM PEs, a plurality of NPU PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs; and transferring the processed data between at least one of the plurality of CIM PEs and at least one of the plurality of NPU PEs via the bus.

    FAST DIGITAL MULTIPLY-ACCUMULATE (MAC) BY FAST DIGITAL MULTIPLICATION CIRCUIT

    公开(公告)号:US20210240447A1

    公开(公告)日:2021-08-05

    申请号:US16778749

    申请日:2020-01-31

    Abstract: Certain aspects provide methods and apparatus for multiplication of digital signals. In accordance with certain aspects, a multiplication circuit may be used to multiply a portion of a first digital input signal with a portion of a second digital input signal via a first multiplier circuit to generate a first multiplication signal, and multiply another portion of the first digital input signal with another portion of the second digital input signal via a second multiplier circuit to generate a second multiplication signal. A third multiplier circuit and multiple adder circuits may be used to generate an output of the multiplication circuit based on the first and second multiplication signals.

    CHARGE-SHARING COMPUTE-IN-MEMORY SYSTEM

    公开(公告)号:US20210133549A1

    公开(公告)日:2021-05-06

    申请号:US16669855

    申请日:2019-10-31

    Abstract: Certain aspects provide a circuit for in-memory computation. The circuit generally includes a first memory cell, and a first computation circuit. The first computation circuit may include a first switch having a control input coupled to an output of the first memory cell, a second switch coupled between a node of the first computation circuit and the first switch, a control input of the second switch being coupled to a discharge word-line (DCWL), a capacitive element coupled between the node and a reference potential node, a third switch coupled between the node and a read bit-line (RBL), and a fourth switch coupled between the node and an activation (ACT) line.

    SELF-ALIGNED VIA FOR GATE CONTACT OF SEMICONDUCTOR DEVICES
    6.
    发明申请
    SELF-ALIGNED VIA FOR GATE CONTACT OF SEMICONDUCTOR DEVICES 审中-公开
    通过半导体器件的栅极接触自对准

    公开(公告)号:US20160005822A1

    公开(公告)日:2016-01-07

    申请号:US14321568

    申请日:2014-07-01

    Abstract: Systems and methods are directed to a three-terminal semiconductor device including a self-aligned via for connecting to a gate terminal Hardmasks and spacers formed over top portions and sidewall portions of a drain connection to a drain terminal and a source connection to a source terminal protect and insulate the drain connection and the source connection, such that short circuits are avoided between the source and drain connections and the self-aligned via. The self-aligned via provides a direct metal-gate connection path between the gate terminal and a metal line such as a M1 metal line while avoiding a separate gate connection layer.

    Abstract translation: 系统和方法涉及三端子半导体器件,其包括用于连接到栅极端子的自对准通孔。硬掩模和形成在到漏极端子的漏极连接的顶部和侧壁部分上的间隔物以及到源极端子的源极连接 保护和绝缘漏极连接和源极连接,从而避免在源极和漏极连接和自对准通孔之间产生短路。 自对准通孔在栅极端子和诸如M1金属线的金属线之间提供直接的金属栅极连接路径,同时避免了单独的栅极连接层。

    PARTIAL SUM MANAGEMENT AND RECONFIGURABLE SYSTOLIC FLOW ARCHITECTURES FOR IN-MEMORY COMPUTATION

    公开(公告)号:US20230047364A1

    公开(公告)日:2023-02-16

    申请号:US17398791

    申请日:2021-08-10

    Abstract: Methods and apparatus for performing machine learning tasks, and in particular, to a neural-network-processing architecture and circuits for improved handling of partial accumulation results in weight-stationary operations, such as operations occurring in compute-in-memory (CIM) processing elements (PEs). One example PE circuit for machine learning generally includes an accumulator circuit, a flip-flop array having an input coupled to an output of the accumulator circuit, a write register, and a first multiplexer having a first input coupled to an output of the write register, having a second input coupled to an output of the flip-flop array, and having an output coupled to a first input of the first accumulator circuit.

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