OUTPUT CIRCUIT FOR ARTIFICIAL NEURAL NETWORK ARRAY

    公开(公告)号:US20240112003A1

    公开(公告)日:2024-04-04

    申请号:US18077993

    申请日:2022-12-08

    IPC分类号: G06N3/063 G06F5/01 G06F7/501

    CPC分类号: G06N3/063 G06F5/01 G06F7/501

    摘要: Numerous examples are disclosed of output circuitry and associated methods in an artificial neural network. In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns, an output block to convert current from columns of the array into a first digital output during a first time period and a second digital output during a second time period, a first output register to store the first digital output during the first time period and to output the stored first digital output during the second time period, and a second output register to store the second digital output during the second time period and to output the stored second digital output during a third time period.

    COMPRESSOR CIRCUIT AND SEMICONDUCTOR INTEGRATED CIRCUIT INCLUDING THE SAME

    公开(公告)号:US20240094987A1

    公开(公告)日:2024-03-21

    申请号:US18329856

    申请日:2023-06-06

    发明人: Byoung Gon Kang

    摘要: A compressor circuit and a semiconductor integrated circuit included the compressor circuit are provided. The semiconductor integrated circuit includes a compressor circuit which includes a first full adder circuit which receives an A1 signal, a B1 signal, and a CI signal to output an IS signal and an ICO signal, and a second full adder circuit which receives a B2 signal, the IS signal, and a CI2 signal to output an S signal and a CO signal. Each of the first full adder circuit and the second full adder circuit are in an L-shaped layout, the first full adder circuit and the second full adder circuit have bent portions that are point-symmetrically engaged with each other, and the compressor circuit is in a rectangular shape. The number of transistors in the second full adder circuit is smaller than the number of transistors in the first full adder circuit.

    Circuits and methods for in-memory computing

    公开(公告)号:US11783875B2

    公开(公告)日:2023-10-10

    申请号:US17828964

    申请日:2022-05-31

    摘要: In some embodiments, an in-memory-computing SRAM macro based on capacitive-coupling computing (C3) (which is referred to herein as “C3SRAM”) is provided. In some embodiments, a C3SRAM macro can support array-level fully parallel computation, multi-bit outputs, and configurable multi-bit inputs. The macro can include circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations in some embodiments. In some embodiments, the macro utilizes analog-mixed-signal capacitive-coupling computing to evaluate the main computations of binary neural networks, binary-multiply-and-accumulate operations. Without needing to access the stored weights by individual row, the macro can assert all of its rows simultaneously and form an analog voltage at the read bitline node through capacitive voltage division, in some embodiments. With one analog-to-digital converter (ADC) per column, the macro cab realize fully parallel vector-matrix multiplication in a single cycle in accordance with some embodiments.