diff --git a/backends/cadence/aot/ops_registrations.py b/backends/cadence/aot/ops_registrations.py
index 3a7bf3ee41b..fa7b7feb208 100644
--- a/backends/cadence/aot/ops_registrations.py
+++ b/backends/cadence/aot/ops_registrations.py
@@ -132,7 +132,11 @@ def quantized_conv_meta(
     out_shift: torch.Tensor,
     channel_last: bool = False,
 ) -> torch.Tensor:
-    out_channels, _in_channels, *kernel_size = weight.shape
+    if channel_last:
+        out_channels, *kernel_size, _ = weight.shape
+    else:
+        out_channels, _, *kernel_size = weight.shape
+
     in_size = input.shape
     # Assert that the input tensor has at least 3 dimensions, and at most 6
     assert len(in_size) > 2
@@ -141,7 +145,13 @@ def quantized_conv_meta(
     # Compute the output tensor size
     output_size = (
         get_conv1d_output_size(
-            in_size, out_channels, stride[1], padding[1], dilation[1], kernel_size[0]
+            in_size,
+            out_channels,
+            stride[1],
+            padding[1],
+            dilation[1],
+            kernel_size[0],
+            channel_last,
         )
         if len(in_size) == 3
         else get_conv2d_output_size(
diff --git a/backends/cadence/aot/utils.py b/backends/cadence/aot/utils.py
index 9e32f3472da..12eb899d9d8 100644
--- a/backends/cadence/aot/utils.py
+++ b/backends/cadence/aot/utils.py
@@ -43,14 +43,20 @@ def get_conv1d_output_size(
     padding: int,
     dilation: int,
     kernel_size: int,
+    channel_last: bool,
 ) -> torch.Size:
     assert len(in_size) == 3
-    N, C, L = in_size
+    if channel_last:
+        N, L, C = in_size
+    else:
+        N, C, L = in_size
 
     # Reference: https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
     lout = (L + 2 * padding - dilation * (kernel_size - 1) - 1) // stride + 1
 
-    return torch.Size((in_size[0], out_channels, lout))
+    if channel_last:
+        return torch.Size((N, lout, out_channels))
+    return torch.Size((N, out_channels, lout))
 
 
 # Get the output size of a 2D convolution given the input size and parameters
@@ -76,7 +82,8 @@ def get_conv2d_output_size(
     wout = (W + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) // stride[
         1
     ] + 1
-
+    if channel_last:
+        return torch.Size((N, hout, wout, out_channels))
     return torch.Size((in_size[0], out_channels, hout, wout))