.. meta:: :description: 3D Convolution using tensor engine with K-replication strategy and W-contiguous tiling. :date-modified: 05/21/2026 .. currentmodule:: nkilib.experimental.conv Conv3D Kernel API Reference =========================== 3D Convolution using tensor engine with K-replication strategy and W-contiguous tiling. Implements a 3D convolution operation (x_in * filters + bias) optimized for NeuronCore using a K-replication strategy for filter loading and W-contiguous tiling for output computation. Supports configurable stride, padding, dilation, optional bias, optional activation function, and LNC sharding. Intended Usage Range: B: 1-128 C_in: 3-1280, C_out: 3-2048 D: 1-1024, H: 1-1024, W: 1-1024 K_d: 1-64, K_h: 1-64, K_w: 1-64 Stride: 1-64 per dimension Dilation: 1-64 per dimension Background ----------- The ``conv3d`` kernel implements 3D convolution using the tensor engine with a K-replication strategy for filter loading and W-contiguous tiling for output computation. It supports configurable stride, padding, dilation, optional bias, activation fusion, and LNC sharding. API Reference -------------- **Source code for this kernel API can be found at**: `conv3d.py `_ conv3d ^^^^^^ .. py:function:: conv3d(x_in: nl.ndarray, filters: nl.ndarray, bias: Optional[nl.ndarray] = None, stride: tuple[int, int, int] = (1, 1, 1), padding: tuple[int, int, int, int, int, int] = (0, 0, 0, 0, 0, 0), dilation: tuple[int, int, int] = (1, 1, 1), activation_fn: Optional[ActFnType] = None, lnc_shard: bool = False) -> nl.ndarray 3D Convolution using tensor engine with K-replication strategy and W-contiguous tiling. :param x_in: [B, C_in, D, H, W], Input tensor on HBM. :type x_in: ``nl.ndarray`` :param filters: [K_d, K_h, K_w, C_in, C_out], Filter weights on HBM. :type filters: ``nl.ndarray`` :param bias: [C_out], Optional bias tensor on HBM. :type bias: ``Optional[nl.ndarray]`` :param stride: (stride_d, stride_h, stride_w), Convolution strides. :type stride: ``tuple[int, int, int]`` :param padding: (pad_d_left, pad_d_right, pad_h_top, pad_h_bottom, pad_w_left, pad_w_right), Padding for each spatial dimension. :type padding: ``tuple[int, int, int, int, int, int]`` :param dilation: (dilation_d, dilation_h, dilation_w), Dilation factors. :type dilation: ``tuple[int, int, int]`` :param activation_fn: Optional activation function to apply after conv. :type activation_fn: ``Optional[ActFnType]`` :param lnc_shard: Enable LNC sharding across neuron cores. :type lnc_shard: ``bool`` :return: [B, C_out, D_out, H_out, W_out], Output tensor on HBM. :rtype: ``nl.ndarray`` **Dimensions**: * B: Batch size * C_in: Number of input channels * C_out: Number of output channels * D: Input depth * H: Input height * W: Input width * K_d: Filter depth * K_h: Filter height * K_w: Filter width * D_out: Output depth = (D + pad_d_left + pad_d_right - dilation_d * (K_d - 1) - 1) // stride_d + 1 * H_out: Output height = (H + pad_h_top + pad_h_bottom - dilation_h * (K_h - 1) - 1) // stride_h + 1