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• Description: Implements 1D convolution where the input and output dimensions are T (temporal or spatial), B (batch), and C (channel).
• Formula: Assume the input self has shape ($H_{\text{in}},N,C_{\text{in}}$) and the output out has shape ($H_{\text{out}},N,C_{\text{out}}$). The output is computed as:$$out_{N_i,C_{out j}} = bias(C_{out j}) + \sum_{k = 0}^{C_{in} - 1} weight(k, C_{out j}) \cdot self(N_i, k)$$ Where $N$ denotes the batch size, $C$ denotes the number of channels, and $H$ denotes the temporal or spatial dimension.

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Each operator has calls. First, aclnnConvTbcGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation flow. Then, aclnnConvTbc is called to perform computation.

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• Parameters

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• Returns

  aclnnStatus: status code. For details, see .

  The first-phase API implements input parameter verification. The following errors may be thrown.

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• Parameters

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• Returns

  aclnnStatus: status code. For details, see .

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• Deterministic computation:

  • aclnnConvTbc defaults to a deterministic implementation.
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The following example is for reference only. For details, see .