diff --git a/tests/python/relay/strategy/arm_cpu/test_conv2d.py b/tests/python/relay/strategy/arm_cpu/test_conv2d.py
index f4fa250ecfe0..8ef9cb09e648 100644
--- a/tests/python/relay/strategy/arm_cpu/test_conv2d.py
+++ b/tests/python/relay/strategy/arm_cpu/test_conv2d.py
@@ -120,48 +120,10 @@ class TestConv2d_NCHW_Spatial_Pack(Conv2dTests):
     schedule_name = parameter("conv2d_nchw_spatial_pack.arm_cpu")
 
 
-in_dtype = tvm.testing.parameter("float16", "float32")
-out_dtype = tvm.testing.parameter("float32")
-
-batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation = tvm.testing.parameters(
-    # Pad M, N, K
-    (1, 1, 1, 1, 1, 1, "SAME", 1),
-    (1, 1, 3, 15, 1, 1, "SAME", 1),
-    # Pad M, K
-    (1, 3, 9, 16, 3, 1, "SAME", 1),
-    # Pad M, N
-    (1, 2, 9, 15, 4, 1, "SAME", 1),
-    # Pad K, N
-    (1, 7, 4, 15, 3, 1, "SAME", 1),
-    # Pad M
-    (1, 2, 9, 16, 4, 1, "SAME", 1),
-    # Pad K
-    (1, 7, 4, 16, 3, 1, "SAME", 1),
-    # Pad N
-    (1, 2, 4, 15, 4, 1, "SAME", 1),
-    (1, 2, 4, 20, 1, 1, "SAME", 1),
-    # Large workloads
-    (1, 128, 32, 128, 3, 1, "SAME", 1),
-    (4, 64, 16, 64, 5, 2, "SAME", 1),
-    (1, 128, 32, 128, 3, 1, "VALID", 1),
-    (4, 64, 16, 64, 5, 2, "VALID", 1),
-    (1, 64, 16, 64, 3, 2, (0, 0, 1, 1), 1),
-    (1, 64, 16, 64, 3, 2, (1, 1, 2, 2), 1),
-    (1, 64, 16, 64, 5, 2, (3, 3, 2, 2), 1),
-    (1, 64, 16, 64, 3, 2, (0, 1, 2, 3), 1),
-    (1, 64, 32, 64, 3, 1, "SAME", 2),
-    (1, 64, 32, 64, 3, 1, (1, 1, 2, 2), 2),
-)
-
-
-@tvm.testing.fixture()
-def ref_data(
-    in_dtype, out_dtype, batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation
-):
+def ref_data(in_dtype, out_dtype, data_shape, num_filter, kernel_size, stride, padding, dilation):
     np.random.seed(0)
-    in_height = in_width = in_size
-    a_shape = (batch, in_height, in_width, in_channel)
-    w_shape = (kernel, kernel, in_channel, num_filter)
+    a_shape = data_shape
+    w_shape = (kernel_size[0], kernel_size[1], data_shape[3], num_filter)
 
     a_np = np.random.uniform(size=a_shape).astype(in_dtype)
     w_np = np.random.uniform(size=w_shape).astype(in_dtype)
@@ -175,9 +137,31 @@ def ref_data(
 @pytest.mark.skipif(
     llvm_version_major() < 16, reason="SME is not supported in earlier versions of LLVM"
 )
+@pytest.mark.parametrize(
+    "data_shape,kernel_size,num_filter,stride,padding,dilation",
+    [
+        ((1, 1, 1, 1), (3, 3), 1, 1, "SAME", 1),
+        ((1, 9, 9, 1), (3, 3), 16, 1, "SAME", 1),
+        ((1, 32, 32, 1), (3, 3), 12, 1, "SAME", 1),
+        ((1, 32, 10, 3), (3, 3), 16, 1, 0, 1),
+        ((1, 49, 10, 1), (10, 4), 64, (2, 1), (4, 1, 5, 1), 1),
+        ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 1),
+        ((1, 32, 32, 16), (3, 4), 16, 1, 0, 1),
+        ((1, 9, 31, 7), (3, 3), 7, 1, "VALID", 1),
+        ((1, 32, 32, 16), (5, 5), 16, 1, (0, 2, 2, 0), 2),
+        ((1, 32, 32, 16), (3, 3), 16, 1, (1, 1, 2, 2), 2),
+        ((1, 134, 153, 32), (3, 3), 2, (2, 2), "VALID", 1),
+        ((1, 16, 16, 64), (1, 1), 8, (1, 1), "SAME", 1),
+    ],
+)
+@pytest.mark.parametrize("in_dtype,out_dtype", [("float32", "float32"), ("float16", "float32")])
 @tvm.testing.requires_aprofile_aem_fvp
-def test_conv2d_sme(target, ref_data, in_dtype, out_dtype, stride, padding, dilation):
-    a_np, w_np, dw_np, b_np = ref_data
+def test_conv2d_sme(
+    target, data_shape, kernel_size, num_filter, stride, padding, dilation, in_dtype, out_dtype
+):
+    a_np, w_np, dw_np, b_np = ref_data(
+        in_dtype, out_dtype, data_shape, num_filter, kernel_size, stride, padding, dilation
+    )
 
     kernel_size = get_const_tuple(w_np.shape[:2])
     out_channels = w_np.shape[3]