Kernel sets normalization with capacitor charge sharing

    公开(公告)号:US11574694B2

    公开(公告)日:2023-02-07

    申请号:US16157848

    申请日:2018-10-11

    Abstract: A method for multiple copies of a set of multi-kernel set operations in a hardware accelerated neural network includes a word line for receiving a pixel value of an input image. A bit line communicates a modified pixel value. An analog memory cell including a first capacitor stores a first kernel weight of a first kernel in one of a plurality of kernel sets such that the pixel value is operated on by the first kernel weight to produce the modified pixel value. A charge connection connects the first capacitor to at least a second capacitor storing a second kernel weight of a related kernel of a second one of the plurality of kernel sets such that charge is shared between the first capacitor and at least the second capacitor to normalize the first kernel weight and the second kernel weight.

    Dynamic management of weight update bit length

    公开(公告)号:US11574196B2

    公开(公告)日:2023-02-07

    申请号:US16596716

    申请日:2019-10-08

    Inventor: Tayfun Gokmen

    Abstract: Machine learning is enhanced by efficiently updating a weight that is represented as a conductivity of a resistive processing unit (RPU) that is connected between a row wire and a column wire. The weight is updated by the RPU interacting with bit streams carried on the row and column wires. Efficiency of the update is enhanced by calculating a bit length for the bit streams as a function of factors that include learning rate η, maximum activity xmax, maximum error differential δmax, and minimum weight update increment Δwmin.

    EXTRACTION OF WEIGHT VALUES IN RESISTIVE PROCESSING UNIT ARRAY

    公开(公告)号:US20220391681A1

    公开(公告)日:2022-12-08

    申请号:US17340242

    申请日:2021-06-07

    Abstract: A system includes a processor, and a resistive processing resistive processing unit coupled to the processor. The resistive processing unit includes an array of cells, wherein the cells respectively include resistive devices, wherein at least a portion of the resistive devices are programmable to store weight values of a given matrix in the array of cells. When the given matrix is stored in the array of cells, the processor is configured to perform a weight extraction process. The weight extraction process applies a set of input vectors to the resistive processing unit to perform analog matrix-vector multiplication operations on the stored matrix, obtains a set of output vectors resulting from the analog matrix-vector multiplication operations, and determines weight values of the given matrix stored in the array of cells utilizing the set of input vectors and the set of output vectors.

    Area and power efficient implementations of modified backpropagation algorithm for asymmetric RPU devices

    公开(公告)号:US11501148B2

    公开(公告)日:2022-11-15

    申请号:US16808811

    申请日:2020-03-04

    Abstract: A device configured to implement an artificial intelligence deep neural network includes a first matrix and a second matrix. The first matrix resistive processing unit (“RPU”) array receives a first input vector along the rows of the first matrix RPU. A second matrix RPU array receives a second input vector along the rows of the second matrix RPU. A reference matrix RPU array receives an inverse of the first input vector along the rows of the reference matrix RPU and an inverse of the second input vector along the rows of the reference matrix RPU. A plurality of analog to digital converters are coupled to respective outputs of a plurality of summing junctions that receive respective column outputs of the first matrix RPU array, the second matrix RPU array, and the reference RPU array and provides a digital value of the output of the plurality of summing junctions.

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