Neural network layer-by-layer debugging

    公开(公告)号:US11308396B2

    公开(公告)日:2022-04-19

    申请号:US16455329

    申请日:2019-06-27

    Abstract: Techniques are disclosed for debugging a neural network execution on a target processor. A reference processor may generate a plurality of first reference tensors for the neural network. The neural network may be repeatedly reduced to produce a plurality of lengths. For each of the lengths, a compiler converts the neural network into first machine instructions, the target processor executes the first machine instructions to generate a first device tensor, and the debugger program determines whether the first device tensor matches a first reference tensor. A shortest length is identified for which the first device tensor does not match the first reference tensor. Tensor output is enabled for a lower-level intermediate representation of the shortest neural network, and the neural network is converted into second machine instructions, which are executed by the target processor to generate a second device tensor.

    NEURAL NETWORK OPERATION REORDERING FOR PARALLEL EXECUTION

    公开(公告)号:US20210247984A1

    公开(公告)日:2021-08-12

    申请号:US17243415

    申请日:2021-04-28

    Abstract: Techniques are disclosed for reordering operations of a neural network to improve runtime efficiency. In some examples, a compiler receives a description of the neural network comprising a plurality of operations. The compiler may determine which execution engine of a plurality of execution engines is to perform each of the plurality of operations. The compiler may determine an order of performance associated with the plurality of operations. The compiler may identify a runtime inefficiency based on the order of performance and a hardware usage for each of the plurality of operations. An operation may be reordered to reduce the runtime inefficiency. Instructions may be compiled based on the plurality of operations, which include the reordered operation.

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