diff --git a/backends/nxp/quantizer/neutron_quantizer.py b/backends/nxp/quantizer/neutron_quantizer.py
new file mode 100644
index 00000000000..eff7f513cb9
--- /dev/null
+++ b/backends/nxp/quantizer/neutron_quantizer.py
@@ -0,0 +1,205 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright 2024-2025 NXP
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from executorch.backends.nxp.quantizer.patterns import (
+    AddmmPattern,
+    AvgPoolPattern,
+    Conv1dPattern,
+    Conv2dPattern,
+    LinearPattern,
+    MaxPoolPattern,
+    PadPattern,
+    PermutePattern,
+    QuantizationPattern,
+    ReluInPlacePattern,
+    ReluPattern,
+    ReshapePattern,
+    SoftMaxPattern,
+)
+from executorch.backends.nxp.quantizer.utils import (
+    find_sequential_partitions_aten,
+    is_annotated,
+    no_outside_users,
+)
+from executorch.backends.xnnpack.quantizer.xnnpack_quantizer_utils import (
+    OperatorConfig,
+    QuantizationAnnotation,
+    QuantizationConfig,
+    QuantizationSpec,
+)
+from torch import fx
+from torch.ao.quantization.observer import HistogramObserver, MinMaxObserver
+from torch.ao.quantization.quantizer import DerivedQuantizationSpec, Quantizer
+from torch.ao.quantization.quantizer.composable_quantizer import ComposableQuantizer
+
+
+class NeutronAtenQuantizer(Quantizer):
+    def __init__(
+        self, pattern: QuantizationPattern, quantization_config: QuantizationConfig
+    ) -> None:
+        super().__init__()
+        self.pattern = pattern
+        self.quantization_config = quantization_config
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        fused_partitions = find_sequential_partitions_aten(
+            model,
+            self.pattern.partition_types(),
+        )
+
+        input_act_qspec = self.quantization_config.input_activation
+        weight_qspec = self.quantization_config.weight
+        bias_qspec = self.quantization_config.bias
+        output_act_qspec = self.quantization_config.output_activation
+
+        for fused_partition in fused_partitions:
+            if not no_outside_users(fused_partition):
+                continue
+
+            anchors = self.pattern.get_anchors(model, fused_partition)
+            if not anchors or anchors.empty:
+                continue
+            if is_annotated(
+                [
+                    x[0]
+                    for x in anchors.inputs
+                    + anchors.weights
+                    + anchors.biases
+                    + anchors.output
+                ]
+            ):
+                continue
+
+            for output, *custom_spec in anchors.output:
+                # pyre-ignore[16]: no attribute
+                output.meta["quantization_annotation"] = QuantizationAnnotation(
+                    # pyre-ignore[6]: incompatible parameter type
+                    output_qspec=(custom_spec[0] if custom_spec else output_act_qspec),
+                    _annotated=True,
+                )
+
+            def annotate_inputs(
+                inputs: Union[
+                    List[Tuple[fx.Node, int]],
+                    List[Tuple[fx.Node, int, DerivedQuantizationSpec],],
+                ],
+                spec: Optional[QuantizationSpec],
+            ) -> None:
+                for node, idx, *custom_spec in inputs:
+                    # pyre-ignore[16]: no attribute
+                    annotation = node.meta.get(
+                        "quantization_annotation",
+                        QuantizationAnnotation(_annotated=True),
+                    )
+                    arg = (
+                        # pyre-ignore[16]: no attribute
+                        node.args[idx]
+                        if isinstance(idx, int)
+                        # pyre-ignore[16]: no attribute
+                        else node.args[idx[0]][idx[1]]
+                    )
+                    annotation.input_qspec_map[arg] = (
+                        custom_spec[0] if custom_spec else spec
+                    )
+                    # pyre-ignore[16]: no attribute
+                    node.meta["quantization_annotation"] = annotation
+
+            def annotate_weights_or_biases(
+                weights_or_biases: List[Tuple[fx.Node, int]],
+                spec: Optional[QuantizationSpec],
+            ) -> None:
+                for node, idx, *custom_spec in weights_or_biases:
+                    annotation = node.meta.get(
+                        "quantization_annotation",
+                        QuantizationAnnotation(_annotated=True),
+                    )
+                    annotation.input_qspec_map[node.args[idx]] = (
+                        custom_spec[0] if custom_spec else spec
+                    )
+                    node.meta["quantization_annotation"] = annotation
+
+            # pyre-ignore[6]: incompatible parameter type
+            annotate_inputs(anchors.inputs, input_act_qspec)
+            annotate_weights_or_biases(anchors.weights, weight_qspec)
+            # pyre-ignore[6]: incompatible parameter type
+            annotate_weights_or_biases(anchors.biases, bias_qspec)
+        return model
+
+    def validate(self, model: fx.GraphModule) -> None:
+        pass
+
+    @classmethod
+    def get_supported_operators(cls) -> List[OperatorConfig]:
+        return []
+
+
+# Quantization Specification used by Neutron NPU
+act_qspec = QuantizationSpec(
+    dtype=torch.int8,
+    quant_min=-128,
+    quant_max=127,
+    qscheme=torch.per_tensor_affine,
+    is_dynamic=False,
+    observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12),
+)
+
+wgt_qspec = QuantizationSpec(
+    dtype=torch.int8,
+    quant_min=-127,
+    quant_max=127,
+    qscheme=torch.per_tensor_symmetric,
+    is_dynamic=False,
+    observer_or_fake_quant_ctr=MinMaxObserver,
+    ch_axis=0,
+)
+
+wgt_fc_qspec = QuantizationSpec(
+    dtype=torch.int8,
+    quant_min=-127,
+    quant_max=127,
+    qscheme=torch.per_tensor_symmetric,
+    is_dynamic=False,
+    observer_or_fake_quant_ctr=MinMaxObserver,
+)
+
+# Is set by the *PatternQuantizer directly.
+bias_qspec = None
+
+
+class NeutronQuantizer(ComposableQuantizer):
+    def __init__(self):
+        static_qconfig = QuantizationConfig(
+            act_qspec,
+            act_qspec,
+            wgt_qspec,
+            None,
+        )
+        static_fc_qconfig = QuantizationConfig(act_qspec, act_qspec, wgt_fc_qspec, None)
+        super().__init__(
+            [
+                NeutronAtenQuantizer(AddmmPattern(), static_fc_qconfig),
+                NeutronAtenQuantizer(Conv1dPattern(), static_qconfig),
+                NeutronAtenQuantizer(Conv2dPattern(), static_qconfig),
+                NeutronAtenQuantizer(LinearPattern(), static_fc_qconfig),
+                NeutronAtenQuantizer(MaxPoolPattern(), static_qconfig),
+                NeutronAtenQuantizer(SoftMaxPattern(), static_qconfig),
+                NeutronAtenQuantizer(ReshapePattern(), static_qconfig),
+                NeutronAtenQuantizer(PermutePattern(), static_qconfig),
+                NeutronAtenQuantizer(PadPattern(), static_qconfig),
+                NeutronAtenQuantizer(ReluPattern(), static_qconfig),
+                NeutronAtenQuantizer(ReluInPlacePattern(), static_qconfig),
+                NeutronAtenQuantizer(AvgPoolPattern(), static_qconfig),
+            ]
+        )
+
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        return model
diff --git a/backends/nxp/quantizer/patterns.py b/backends/nxp/quantizer/patterns.py
new file mode 100644
index 00000000000..6797447c50c
--- /dev/null
+++ b/backends/nxp/quantizer/patterns.py
@@ -0,0 +1,342 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright 2025 NXP
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import List, Optional, Tuple, Type, Union
+
+import torch
+
+from executorch.backends.nxp.quantizer.utils import get_bias_qparams
+from torch import fx
+from torch._ops import OpOverload
+from torch.ao.quantization.quantizer import (
+    DerivedQuantizationSpec,
+    FixedQParamsQuantizationSpec,
+    SharedQuantizationSpec,
+)
+
+
+@dataclass
+class PartitionAnchors:
+    """
+    All fields except output are lists of (node, args_index) pair, where node is from
+    the given partition and node.args[args_index] is an input to the partition. Assumes
+    a single output.
+
+    Quantizer uses inputs, weights and biases for quantization annotation. The others
+    field contains tensor inputs that aren't quantized, and the literals fields contains
+    is used for other types of input values as well as handling default parameters.
+    """
+
+    # Inputs can share quantization parameters
+    inputs: List[
+        Union[
+            Tuple[fx.Node, Union[int, Tuple[int, int]]],
+            Tuple[
+                fx.Node,
+                Union[int, Tuple[int, int]],
+                SharedQuantizationSpec,
+            ],
+        ]
+    ] = field(default_factory=list)
+    weights: List[Tuple[fx.Node, int]] = field(default_factory=list)
+    biases: List[
+        Union[Tuple[fx.Node, int], Tuple[fx.Node, int, DerivedQuantizationSpec]]
+    ] = field(default_factory=list)
+    others: List[Tuple[fx.Node, int]] = field(default_factory=list)
+    literals: List[Tuple[fx.Node, int]] = field(default_factory=list)
+    output: List[Union[Tuple[fx.Node], Tuple[fx.Node, SharedQuantizationSpec]]] = field(
+        default_factory=list
+    )
+    empty: bool = False
+
+
+class QuantizationPattern(ABC):
+    @abstractmethod
+    def partition_types(self) -> list[OpOverload]:
+        """
+        List of types to be passed to find_sequential_partitions_aten.
+        """
+        pass
+
+    @abstractmethod
+    def get_anchors(
+        self, gm: torch.fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> Optional[PartitionAnchors]:
+        pass
+
+
+class SharedSpecPattern(QuantizationPattern):
+    """
+    Quantization pattern for shared quantization.
+
+    The quantization is derived from the previous node quantization and the input and output shares the same
+    quantization parameters (scale and zero-point).
+    """
+
+    def partition_types(self) -> List[Type[torch.nn.Module]]:
+        pass
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors | None:
+        node = fused_partition[0].nodes[-1]
+        assert len(fused_partition[0].input_nodes) == 1
+        prev_node = fused_partition[0].input_nodes[0]
+
+        # Previous node was not quantized => we are not able to share q-params
+        if "quantization_annotation" not in prev_node.meta:
+            return None
+
+        qspec = SharedQuantizationSpec(prev_node)
+
+        return PartitionAnchors(
+            inputs=[(node, 0)],
+            weights=[],
+            biases=[],
+            output=[
+                (node, qspec),
+            ],
+        )
+
+
+class AddmmPattern(QuantizationPattern):
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.addmm.default]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors:
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        addmm_node = fused_partition[0].nodes[-1]
+
+        bias_qspec = DerivedQuantizationSpec(
+            derived_from=[
+                (addmm_node.args[1], addmm_node),
+                (addmm_node.args[2], addmm_node),
+            ],
+            derive_qparams_fn=get_bias_qparams,
+            dtype=torch.int32,
+            quant_min=-(2**31),
+            quant_max=2**31 - 1,
+            qscheme=torch.per_tensor_affine,
+        )
+
+        return PartitionAnchors(
+            inputs=[(addmm_node, 1)],
+            weights=[(addmm_node, 2)],
+            biases=[(addmm_node, 0, bias_qspec)],
+            output=[(addmm_node,)],
+        )
+
+
+class AvgPoolPattern(SharedSpecPattern):
+    """
+    Quantizer for AvgPool2D operator.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.avg_pool2d.default]
+
+
+class Conv1dPattern(QuantizationPattern):
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.conv1d.default]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors:
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        conv1d_node = fused_partition[0].nodes[-1]
+
+        bias_qspec = DerivedQuantizationSpec(
+            derived_from=[
+                (conv1d_node.args[0], conv1d_node),
+                (conv1d_node.args[1], conv1d_node),
+            ],
+            derive_qparams_fn=get_bias_qparams,
+            dtype=torch.int32,
+            quant_min=-(2**31),
+            quant_max=2**31 - 1,
+            qscheme=torch.per_tensor_affine,
+        )
+
+        # Keep bias empty if not supplied
+        bias = []
+        if len(conv1d_node.args) > 2 and conv1d_node.args[2] is not None:
+            bias = [(conv1d_node, 2, bias_qspec)]
+
+        return PartitionAnchors(
+            inputs=[(conv1d_node, 0)],
+            weights=[(conv1d_node, 1)],
+            # pyre-fixme[6]: Incompatible parameter type
+            biases=bias,
+            output=[(conv1d_node,)],
+        )
+
+
+class Conv2dPattern(QuantizationPattern):
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.conv2d.default]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors:
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        conv2d_node = fused_partition[0].nodes[-1]
+
+        bias_qspec = DerivedQuantizationSpec(
+            derived_from=[
+                (conv2d_node.args[0], conv2d_node),
+                (conv2d_node.args[1], conv2d_node),
+            ],
+            derive_qparams_fn=get_bias_qparams,
+            dtype=torch.int32,
+            quant_min=-(2**31),
+            quant_max=2**31 - 1,
+            qscheme=torch.per_tensor_affine,
+        )
+
+        # Keep bias empty if not supplied
+        bias = []
+        if len(conv2d_node.args) > 2 and conv2d_node.args[2] is not None:
+            bias = [(conv2d_node, 2, bias_qspec)]
+
+        return PartitionAnchors(
+            inputs=[(conv2d_node, 0)],
+            weights=[(conv2d_node, 1)],
+            # pyre-fixme[6]: Incompatible parameter type
+            biases=bias,
+            output=[(conv2d_node,)],
+        )
+
+
+class LinearPattern(QuantizationPattern):
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.linear.default]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors:
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        linear_node = fused_partition[0].nodes[-1]
+
+        bias_qspec = DerivedQuantizationSpec(
+            derived_from=[
+                (linear_node.args[0], linear_node),
+                (linear_node.args[1], linear_node),
+            ],
+            derive_qparams_fn=get_bias_qparams,
+            dtype=torch.int32,
+            quant_min=-(2**31),
+            quant_max=2**31 - 1,
+            qscheme=torch.per_tensor_affine,
+        )
+
+        # Keep bias empty if not supplied
+        bias = []
+        if len(linear_node.args) > 2:
+            bias = [(linear_node, 2, bias_qspec)]
+
+        return PartitionAnchors(
+            inputs=[(linear_node, 0)],
+            weights=[(linear_node, 1)],
+            # pyre-fixme[6]: Incompatible parameter type
+            biases=bias,
+            output=[(linear_node,)],
+        )
+
+
+class MaxPoolPattern(SharedSpecPattern):
+    """
+    Quantizer for MaxPool2D operator.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.max_pool2d.default]
+
+
+class PadPattern(SharedSpecPattern):
+    """
+    Quantizer for Pad operator.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.pad.default]
+
+
+class PermutePattern(SharedSpecPattern):
+    """
+    Quantizer for Permute operator.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.permute.default]
+
+
+class ReluPattern(SharedSpecPattern):
+    """
+    Quantizer for Relu operator. Shared quantization spec is selected, as ReLU usually follows computation layer.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.relu.default]
+
+
+class ReluInPlacePattern(SharedSpecPattern):
+    """
+    Quantizer for Relu operator with param inplace=True. Shared quantization spec is selected, as ReLU usually
+    follows computation layer.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.relu_.default]
+
+
+class ReshapePattern(SharedSpecPattern):
+    """
+    Quantizer for Reshape operator.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.reshape.default]
+
+
+class SoftMaxPattern(QuantizationPattern):
+    """
+    Quantizer for Softmax operator.
+
+    The quantization of Softmax output is fixed to scale 1/256, zero point -128, dtype int8.
+    """
+
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.softmax.int]
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> PartitionAnchors:
+        node = fused_partition[0].nodes[-1]
+        assert len(fused_partition[0].input_nodes) == 1
+
+        qspec = FixedQParamsQuantizationSpec(
+            dtype=torch.int8,
+            scale=1.0 / 256.0,
+            zero_point=-128,
+            quant_min=-128,
+            quant_max=127,
+            qscheme=torch.per_tensor_affine,
+        )
+
+        return PartitionAnchors(
+            inputs=[(node, 0)],
+            weights=[],
+            biases=[],
+            output=[
+                (node, qspec),
+            ],
+        )
diff --git a/backends/nxp/quantizer/utils.py b/backends/nxp/quantizer/utils.py
new file mode 100644
index 00000000000..1effcdff25a
--- /dev/null
+++ b/backends/nxp/quantizer/utils.py
@@ -0,0 +1,151 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright 2024-2025 NXP
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+import itertools
+from collections import OrderedDict
+from typing import Any, Dict, List, Tuple, Type
+
+import torch
+from torch import fx
+from torch._ops import OpOverload
+from torch.ao.quantization import ObserverOrFakeQuantize
+from torch.fx.passes.utils.source_matcher_utils import (
+    check_subgraphs_connected,
+    SourcePartition,
+)
+
+
+def is_annotated(nodes: List[fx.Node]) -> bool:
+    annotated = False
+    for node in nodes:
+        annotated = annotated or (
+            "quantization_annotation" in node.meta
+            and node.meta["quantization_annotation"]._annotated
+        )
+    return annotated
+
+
+def no_outside_users(fused_partition) -> bool:
+    """
+    Checks if each partition other than the last does not have any outside users.
+    """
+    for source_partition in fused_partition[:-1]:
+        if len(source_partition.output_nodes) != 1:
+            return False
+        if len(source_partition.output_nodes[0].users) != 1:
+            return False
+    return True
+
+
+def get_bias_qparams(
+    obs_or_fqs: List[ObserverOrFakeQuantize],
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    act_scale, _ = obs_or_fqs[0].calculate_qparams()
+    weight_scale, _ = obs_or_fqs[1].calculate_qparams()
+    bias_scale = act_scale * weight_scale
+    bias_zero_point = torch.zeros_like(bias_scale, dtype=torch.int32)
+    return bias_scale, bias_zero_point
+
+
+def get_aten_node_target_partitions(
+    graph: torch.fx.Graph,
+    wanted_original_aten_op: List[OpOverload],
+):
+    """
+    Args:
+        graph: The graph we want to partition
+        wanted_original_aten_op: List of original_aten ops (OpOverload)
+
+    Returns:
+        Dictionary mapping aten ops that were given to a list of SourcePartitions
+        that correspond to the list of nodes that were decomposed from the given
+        aten ops.
+    """
+    modules: Dict[Type, Dict[str, List[torch.fx.Node]]] = {}
+
+    for node in graph.nodes:
+        # The metadata source_fn should contain a tuple of a unique name for the
+        # source, and the source function if the node is decomposed from a
+        # function, or the type of module if the node is decomposed from a leaf
+        # module
+        # TODO(matthiascremon): look into ways to avoid using source_fn_stack
+        if (source_fn_st := node.meta.get("source_fn_stack")) is None:
+            continue
+
+        source_fn = source_fn_st[-1]
+        if node.target not in wanted_original_aten_op:
+            continue
+
+        diff_modules = modules.setdefault(source_fn[1], {})
+        partition = diff_modules.setdefault(node.name, [])
+        partition.append(node)
+
+    def make_partition(
+        nodes: List[torch.fx.Node], module_type: Type
+    ) -> SourcePartition:
+        input_nodes = set()
+        output_nodes = set()
+        params = set()
+        for node in nodes:
+            for arg in node.args:
+                if isinstance(arg, torch.fx.Node) and arg not in nodes:
+                    input_nodes.add(arg)
+
+            if node.op == "get_attr":
+                params.add(node)
+
+            for user in node.users.keys():
+                if user not in nodes:
+                    output_nodes.add(node)
+
+        return SourcePartition(
+            nodes,
+            module_type,
+            list(input_nodes),
+            list(output_nodes),
+            list(params),  # type: ignore[arg-type]
+        )
+
+    ret: Dict[Type[Any], List[SourcePartition]] = {}
+
+    for k, v in modules.items():
+        ret[k] = [make_partition(partition, k) for partition in v.values()]
+
+    return ret
+
+
+def _partitions_sequential(partitions: Tuple[SourcePartition]) -> bool:
+    prev_partition = None
+    for partition in partitions:
+        if prev_partition is not None and not check_subgraphs_connected(
+            prev_partition, partition
+        ):
+            return False
+        prev_partition = partition
+    return True
+
+
+def find_sequential_partitions_aten(
+    gm: torch.fx.GraphModule,
+    partition_types: List[Any],
+):
+    typed_partitions: OrderedDict[Any, List[SourcePartition]] = OrderedDict()
+    for partition_type in partition_types:
+        partitions = get_aten_node_target_partitions(gm.graph, [partition_type])
+        typed_partitions[partition_type] = list(
+            itertools.chain.from_iterable(partitions.values())
+        )
+
+    typed_partitions_list = list(typed_partitions.values())
+    fusion_candidates = itertools.product(*typed_partitions_list)
+    fused_partitions = []
+    for candidate in fusion_candidates:
+        if _partitions_sequential(candidate):
+            fused_partitions.append(candidate)
+    return fused_partitions
diff --git a/backends/nxp/tests/models.py b/backends/nxp/tests/models.py
new file mode 100644
index 00000000000..741e64a28a1
--- /dev/null
+++ b/backends/nxp/tests/models.py
@@ -0,0 +1,238 @@
+# Copyright 2024 NXP
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Collection, Union
+
+import torch
+
+
+class Conv2dModule(torch.nn.Module):
+    def __init__(
+        self,
+        bias: bool = True,
+        dilation: Union[int, tuple[int, int]] = 1,
+        in_channels: int = 4,
+        kernel_size: Union[int, tuple[int, int]] = 3,
+        out_channels: int = 8,
+        padding: Union[str, int, Collection[int]] = 0,
+        stride: Union[int, tuple[int, int]] = 2,
+    ):
+        super().__init__()
+
+        self.conv = torch.nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class Conv2dAndMaxPool2DModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.conv = torch.nn.Conv2d(
+            in_channels=8, out_channels=32, kernel_size=5, bias=True
+        )
+        self.maxpool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.maxpool(x)
+
+
+class Conv2dConstantPadNDModule(torch.nn.Module):
+    def __init__(self, paddings: Collection[int], constant: float | int | None = None):
+        super().__init__()
+        self.pad = ConstantPadNDModule(paddings, constant)
+        self.conv = Conv2dModule()
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.pad(x)
+
+
+class SoftmaxModule(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+
+        self.softmax = torch.nn.Softmax(dim=dim)
+
+    def forward(self, x):
+        return self.softmax(x)
+
+
+class SoftmaxConvModule(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+
+        self.conv = Conv2dModule()
+        self.softmax = SoftmaxModule(dim=dim)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.softmax(x)
+
+
+class LinearModule(torch.nn.Module):
+    def __init__(self, bias: bool):
+        super().__init__()
+        self.linear = torch.nn.Linear(32, 16, bias=bias)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+class LinearSoftmaxModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.linear = torch.nn.Linear(12, 10)
+        self.softmax = torch.nn.Softmax(1)
+
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.softmax(x)
+
+        return x
+
+
+class ConvFCSoftmaxModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.conv = torch.nn.Conv2d(4, 64, 2, bias=False)
+        self.fc = torch.nn.Linear(1024, 10)
+        self.softmax = torch.nn.Softmax(1)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = torch.reshape(x, (-1, 1024))
+        x = self.fc(x)
+        x = self.softmax(x)
+
+        return x
+
+
+class ConstantPadNDModule(torch.nn.Module):
+    def __init__(self, paddings: Collection[int], constant: float | int | None = None):
+        super().__init__()
+        self.paddings = paddings
+        self.constant = constant
+
+    def forward(self, x):
+        if self.constant is None:
+            return torch.nn.functional.pad(x, tuple(self.paddings), "constant")
+        else:
+            return torch.nn.functional.pad(
+                x, tuple(self.paddings), "constant", self.constant
+            )
+
+
+class ConstantPadNDConvModule(torch.nn.Module):
+    def __init__(self, paddings: Collection[int], constant: float | int | None = None):
+        super().__init__()
+        self.pad = ConstantPadNDModule(paddings, constant)
+        self.conv = Conv2dModule()
+
+    def forward(self, x):
+        x = self.pad(x)
+        return self.conv(x)
+
+
+class MaxPool2dModule(torch.nn.Module):
+    def __init__(self, padding=0):
+        super().__init__()
+
+        self.max_pool2d = torch.nn.MaxPool2d(
+            kernel_size=3, stride=2, padding=padding, dilation=1
+        )
+
+    def forward(self, x):
+        return self.max_pool2d(x)
+
+
+class MaxPool2dConvModule(torch.nn.Module):
+    def __init__(self, padding=0):
+        super().__init__()
+
+        self.conv = Conv2dModule()
+        self.max_pool2d = torch.nn.MaxPool2d(
+            kernel_size=3, stride=2, padding=padding, dilation=1
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.max_pool2d(x)
+
+
+class AvgPool2dModule(torch.nn.Module):
+    def __init__(self, count_include_pad, padding=0):
+        super().__init__()
+
+        self.avg_pool = torch.nn.AvgPool2d(
+            kernel_size=3,
+            stride=2,
+            padding=padding,
+            count_include_pad=count_include_pad,
+        )
+
+    def forward(self, x):
+        return self.avg_pool(x)
+
+
+class AvgPool2dConvModule(torch.nn.Module):
+    def __init__(self, count_include_pad, padding=0):
+        super().__init__()
+
+        self.conv = Conv2dModule()
+        self.avg_pool = torch.nn.AvgPool2d(
+            kernel_size=3,
+            stride=1,
+            padding=padding,
+            count_include_pad=count_include_pad,
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.avg_pool(x)
+
+
+class ReLUModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x):
+        return self.relu(x)
+
+
+class Conv2dReLUModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.conv = torch.nn.Conv2d(4, 64, 2, bias=False)
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        return self.relu(x)
+
+
+class Conv2dPermuteModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(4, 64, 2, bias=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.permute(x, [0, 2, 1, 3])
diff --git a/backends/nxp/tests/test_quantizer.py b/backends/nxp/tests/test_quantizer.py
new file mode 100644
index 00000000000..868a94059b5
--- /dev/null
+++ b/backends/nxp/tests/test_quantizer.py
@@ -0,0 +1,273 @@
+# Copyright 2024 NXP
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Tests for NeutronQuantizer.
+
+import executorch.backends.nxp.tests.models as models
+import torch
+from executorch.backends.nxp.quantizer.neutron_quantizer import NeutronQuantizer
+from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
+
+
+def _get_target_name(node):
+    return node._pretty_print_target(node.target)
+
+
+def test_quantizer_conv2d():
+    model = models.Conv2dModule()
+    model.eval()
+
+    example_input = (torch.ones(1, 4, 32, 32),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 11
+    assert nodes[7].name == "conv2d"
+    # [0]: Input, [1] : weights, [2]: bias
+    assert (
+        _get_target_name(nodes[7].args[0])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[7].args[1])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[7].args[2])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[8])
+        == "torch.ops.quantized_decomposed.quantize_per_tensor.default"
+    )
+    assert nodes[8].args[0].name == "conv2d"
+
+
+def test_quantizer_linear():
+    model = models.LinearModule(bias=True)
+    model.eval()
+
+    example_input = (torch.ones(10, 32),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 11
+    assert nodes[7].name == "linear"
+    # [0]: Input, [1] : weights, [2]: bias
+    assert (
+        _get_target_name(nodes[7].args[0])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[7].args[1])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[7].args[2])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[8])
+        == "torch.ops.quantized_decomposed.quantize_per_tensor.default"
+    )
+    assert nodes[8].args[0].name == "linear"
+
+
+def test_quantizer_maxpool2d():
+    model = models.Conv2dAndMaxPool2DModule()
+    model.eval()
+
+    example_input = (torch.ones(1, 8, 32, 32),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 14
+    # Check if QDQ pattern:
+    assert nodes[10].name == "max_pool2d"
+    assert (
+        _get_target_name(nodes[10].args[0])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[11])
+        == "torch.ops.quantized_decomposed.quantize_per_tensor.default"
+    )
+    assert nodes[11].args[0].name == "max_pool2d"
+
+    # Check if input and output quantization is same
+    input_quant = nodes[10].args[0].args[1:]
+    output_quant = nodes[11].args[1:]
+    assert input_quant == output_quant
+
+
+def test_quantizer_softmax():
+    model = models.SoftmaxModule(dim=0)
+    model.eval()
+
+    example_input = (torch.ones(1, 10),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 7
+    # Check if QDQ pattern:
+    assert nodes[3].name == "softmax"
+    assert (
+        _get_target_name(nodes[3].args[0])
+        == "torch.ops.quantized_decomposed.dequantize_per_tensor.default"
+    )
+    assert (
+        _get_target_name(nodes[4])
+        == "torch.ops.quantized_decomposed.quantize_per_tensor.default"
+    )
+    assert nodes[4].args[0].name == "softmax"
+
+    # Check output quantization
+    scale, zp, _, _, dtype = nodes[4].args[1:]
+    assert scale == 1.0 / 256.0
+    assert zp == -128
+    assert dtype == torch.int8
+
+
+def test_quantizer_single_maxpool2d():
+    model = models.MaxPool2dModule()
+    model.eval()
+
+    example_input = (torch.ones(1, 4, 32, 32),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 3
+    assert nodes[1].name == "max_pool2d"
+    assert "quantization_annotation" not in nodes[1].meta
+
+
+def test_quantizer_conv2d_relu():
+    model = models.Conv2dReLUModule()
+    model.eval()
+
+    example_input = (torch.ones(1, 4, 32, 32),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 12
+    assert nodes[7].name == "dequantize_per_tensor_default_2"
+    assert nodes[8].name == "relu"
+    assert nodes[9].name == "quantize_per_tensor_default_3"
+
+
+def test_quantizer_conv2d_avg_pool2d():
+    model = models.AvgPool2dConvModule(count_include_pad=False)
+    model.eval()
+
+    example_input = (torch.ones(1, 4, 16, 16),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 14
+    assert nodes[9].name == "dequantize_per_tensor_default_3"
+    assert nodes[10].name == "avg_pool2d"
+    assert nodes[11].name == "quantize_per_tensor_default_4"
+
+
+def test_quantizer_conv2d_permute():
+    model = models.Conv2dPermuteModule()
+    model.eval()
+
+    example_input = (torch.ones(1, 4, 16, 16),)
+    quantizer = NeutronQuantizer()
+    graph_module = torch.export.export_for_training(
+        model, example_input, strict=True
+    ).module()
+
+    # noinspection PyTypeChecker
+    m = prepare_pt2e(graph_module, quantizer)
+    m(*example_input)
+    m = convert_pt2e(m)
+
+    # Dry run
+    m(*example_input)
+
+    nodes = list(m.graph.nodes)
+    assert len(nodes) == 12
+    assert nodes[7].name == "dequantize_per_tensor_default_2"
+    assert nodes[8].name == "permute"
+    assert nodes[9].name == "quantize_per_tensor_default_3"