METHODS AND SYSTEMS FOR DISTRIBUTED TRAINING A DEEP NEURAL NETWORK

    公开(公告)号:US20250045599A1

    公开(公告)日:2025-02-06

    申请号:US18884948

    申请日:2024-09-13

    Abstract: Some embodiments of the present application provide a forward-propagation-only (FP-only) method of training a DNN model. Such methods result in a trained DNN model whose performance comparable to a DNN model trained using bidirectional training methods. The FP-only method for training a DNN model may operate without employing the known chain rule. The chain rule is employed when computing gradients for a backward propagation in a bidirectional method. The FP-only method may allow for the computations and updates to the parameters for each layer of the DNN model to be performed in parallel. The FP-only methods for training a DNN model use stochastic gradient descent and the FP-only method for training a DNN model still involves computing gradients. However, the FP-only methods of training a DNN model allow for computing of gradients without the chain rule.

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