Invention Grant
- Patent Title: Row-by-row convolutional neural network mapping for analog artificial intelligence network training
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Application No.: US16884130Application Date: 2020-05-27
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Publication No.: US12050997B2Publication Date: 2024-07-30
- Inventor: Hsinyu Tsai , Geoffrey Burr , Pritish Narayanan , Malte Johannes Rasch
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: CANTOR COLBURN LLP
- Agent Daniel Yeates
- Main IPC: G06N3/084
- IPC: G06N3/084 ; G11C7/10 ; G11C11/54 ; G11C13/00 ; G06N3/063

Abstract:
A computer implemented method for implementing a convolutional neural network (CNN) using a crosspoint array includes configuring the crosspoint array to implement a convolution layer by storing one or more weights in crosspoint devices of the array. The method further includes making multiple copies of the weights and training the CNN. Training the CNN includes mapping input data of the convolution layer to the crosspoint array in a row-by-row manner. Further the excitation is input in a row-by-row manner into the crosspoint array, thereby creating row-by-row forward output from the crosspoint array. Further, outputs from the crosspoint devices are stored to corresponding integrators. Errors in the outputs as compared to a desired output, from multiple rows are computed and back propagated in a row-by-row manner into the crosspoint array, the computed errors transmitted to a previous convolution layer.
Public/Granted literature
- US20210374546A1 ROW-BY-ROW CONVOLUTIONAL NEURAL NETWORK MAPPING FOR ANALOG ARTIFICIAL INTELLIGENCE NETWORK TRAINING Public/Granted day:2021-12-02
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