diff --git a/elasticai/creator/nn/__init__.py b/elasticai/creator/nn/__init__.py
index a5d7cbb5..fbb5502e 100644
--- a/elasticai/creator/nn/__init__.py
+++ b/elasticai/creator/nn/__init__.py
@@ -1,3 +1,4 @@
+from .batch_normed_conv1d import FPBatchNormedConv1d
 from .batch_normed_linear import FPBatchNormedLinear
 from .conv1d import FPConv1d
 from .hard_sigmoid import FPHardSigmoid
diff --git a/elasticai/creator/nn/batch_normed_conv1d/__init__.py b/elasticai/creator/nn/batch_normed_conv1d/__init__.py
new file mode 100644
index 00000000..827e9a69
--- /dev/null
+++ b/elasticai/creator/nn/batch_normed_conv1d/__init__.py
@@ -0,0 +1 @@
+from .layer import FPBatchNormedConv1d
diff --git a/elasticai/creator/nn/batch_normed_conv1d/layer.py b/elasticai/creator/nn/batch_normed_conv1d/layer.py
new file mode 100644
index 00000000..fc80f61a
--- /dev/null
+++ b/elasticai/creator/nn/batch_normed_conv1d/layer.py
@@ -0,0 +1,113 @@
+import math
+from typing import Any, cast
+
+import torch.nn
+
+from elasticai.creator.base_modules.arithmetics.fixed_point_arithmetics import (
+    FixedPointArithmetics,
+)
+from elasticai.creator.base_modules.conv1d import Conv1d
+from elasticai.creator.base_modules.two_complement_fixed_point_config import (
+    FixedPointConfig,
+)
+from elasticai.creator.nn.conv1d.design import FPConv1d as FPConv1dDesign
+from elasticai.creator.vhdl.translatable import Translatable
+
+
+class FPBatchNormedConv1d(Translatable, torch.nn.Module):
+    def __init__(
+        self,
+        total_bits: int,
+        frac_bits: int,
+        in_channels: int,
+        out_channels: int,
+        signal_length: int,
+        kernel_size: int | tuple[int],
+        bn_eps: float = 1e-5,
+        bn_momentum: float = 0.1,
+        bn_affine: bool = True,
+        stride: int | tuple[int] = 1,
+        padding: int | tuple[int] | str = 0,
+        bias: bool = True,
+        device: Any = None,
+    ) -> None:
+        super().__init__()
+        self._arithmetics = FixedPointArithmetics(
+            config=FixedPointConfig(total_bits=total_bits, frac_bits=frac_bits)
+        )
+        self._signal_length = signal_length
+        self._conv1d = Conv1d(
+            arithmetics=self._arithmetics,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=bias,
+            device=device,
+        )
+        self._batch_norm = torch.nn.BatchNorm1d(
+            num_features=out_channels,
+            eps=bn_eps,
+            momentum=bn_momentum,
+            affine=bn_affine,
+            track_running_stats=True,
+            device=device,
+        )
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        has_batches = inputs.dim() == 2
+        input_shape = (
+            (inputs.shape[0], self.in_channels, -1)
+            if has_batches
+            else (self.in_channels, -1)
+        )
+        output_shape = (inputs.shape[0], -1) if has_batches else (-1,)
+        x = inputs.view(*input_shape)
+        x = self._conv1d(x)
+        x = self._batch_norm(x)
+        x = self._arithmetics.quantize(x)
+        outputs = x.view(*output_shape)
+        return outputs
+
+    def translate(self, name: str) -> FPConv1dDesign:
+        def float_to_signed_int(value: float | list) -> int | list:
+            if isinstance(value, list):
+                return list(map(float_to_signed_int, value))
+            return self._arithmetics.config.as_integer(value)
+
+        def flatten_tuple(x: int | tuple[int, ...]) -> int:
+            return x[0] if isinstance(x, tuple) else x
+
+        bn_mean = cast(torch.Tensor, self._batch_norm.running_mean)
+        bn_variance = cast(torch.Tensor, self._batch_norm.running_var)
+        bn_epsilon = self._batch_norm.eps
+        conv_weight = self._conv1d.weight
+        conv_bias = (
+            torch.tensor([0] * self._conv1d.out_channels)
+            if self._conv1d.bias is None
+            else self._conv1d.bias
+        )
+
+        std = torch.sqrt(bn_variance + bn_epsilon)
+        weights = conv_weight / std
+        bias = (conv_bias - bn_mean) / std
+
+        if self._batch_norm.affine:
+            weights = (self._batch_norm.weight * weights.t()).t()
+            bias = self._batch_norm.weight * bias + self._batch_norm.bias
+
+        return FPConv1dDesign(
+            name=name,
+            total_bits=self._config.total_bits,
+            frac_bits=self._config.frac_bits,
+            in_channels=self._conv1d.in_channels,
+            out_channels=self._conv1d.out_channels,
+            signal_length=self._signal_length,
+            kernel_size=flatten_tuple(self._conv1d.kernel_size),
+            weights=float_to_signed_int(weights.tolist()),
+            bias=float_to_signed_int(bias.tolist()),
+            stride=flatten_tuple(self._conv1d.stride),
+            padding=flatten_tuple(self._conv1d.padding),
+            dilation=flatten_tuple(self._conv1d.dilation),
+        )