Invention Application
- Patent Title: LOW-OVERHEAD ERROR PREDICTION AND PREEMPTION IN DEEP NEURAL NETWORK USING APRIORI NETWORK STATISTICS
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Application No.: US16262832Application Date: 2019-01-30
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Publication No.: US20200241954A1Publication Date: 2020-07-30
- Inventor: Swagath Venkataramani , Schuyler Eldridge , Karthik V. Swaminathan , Alper Buyuktosunoglu , Pradip Bose
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Main IPC: G06F11/07
- IPC: G06F11/07 ; G06N3/08 ; G06N3/04

Abstract:
A coarse error correction system for detecting, predicting, and correcting errors in neural networks is provided. The coarse error correction system receives a first set of statistics that are computed from values collected from a neural network during a training phase of the neural network. The coarse error correction system computes a second set of statistics based on values collected from the neural network during a run-time phase of the neural network. The coarse error correction system detects an error in the neural network during the run-time phase of the neural network by comparing the first set of statistics with the second set of statistics. The coarse error correction system increases a voltage setting to the neural network based on the detected error.
Public/Granted literature
- US11016840B2 Low-overhead error prediction and preemption in deep neural network using apriori network statistics Public/Granted day:2021-05-25
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