diff --git a/python/tvm/testing/utils.py b/python/tvm/testing/utils.py
index 38b39b5fc27c..84b631cf3823 100644
--- a/python/tvm/testing/utils.py
+++ b/python/tvm/testing/utils.py
@@ -1057,6 +1057,13 @@ def _has_cpu_feat(features):
 )
 
 
+requires_arm_fp16 = Feature(
+    "arm_fp16",
+    "Arm(R) Neon(TM) instructions for FP16",
+    run_time_check=lambda: _has_cpu_feat("fullfp16"),
+)
+
+
 requires_aarch64_sve = Feature(
     "arm_sve",
     "AArch64 SVE",
diff --git a/tests/python/topi/test_topi_conv2d_nhwc.py b/tests/python/topi/test_topi_conv2d_nhwc.py
index 6ff844de088f..b5c9518d3419 100644
--- a/tests/python/topi/test_topi_conv2d_nhwc.py
+++ b/tests/python/topi/test_topi_conv2d_nhwc.py
@@ -53,7 +53,7 @@
         topi.arm_cpu.schedule_conv2d_nhwc_spatial_pack,
     ),
     (
-        "llvm --device arm_cpu --mtriple aarch64-linux-gnu -mattr=+v8.2a",
+        "llvm --device arm_cpu --mtriple aarch64-linux-gnu -mattr=+v8.2a,+fullfp16",
         topi.arm_cpu.compute_conv2d_NHWC_hybrid,
         topi.arm_cpu.schedule_conv2d_NHWC_hybrid,
     ),
@@ -64,7 +64,7 @@
     ),
 )
 
-dtype = tvm.testing.parameter("float32")
+dtype = tvm.testing.parameter("float16", "float32")
 
 batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation = tvm.testing.parameters(
     # Pad M, N, K
@@ -104,14 +104,36 @@ def ref_data(dtype, batch, in_channel, in_size, num_filter, kernel, stride, padd
     a_shape = (batch, in_height, in_width, in_channel)
     w_shape = (kernel, kernel, in_channel, num_filter)
 
+    np.random.seed(0)
     a_np = np.random.uniform(size=a_shape).astype(dtype)
     w_np = np.random.uniform(size=w_shape).astype(dtype)
     dw_np = tvm.topi.testing.dilate_python(w_np, (dilation, dilation, 1, 1))
-    b_np = tvm.topi.testing.conv2d_nhwc_python(a_np, dw_np, stride, padding)
+
+    # scipy.signal.convolve2d does not support float16 data types,
+    # and the python fallback would be too slow for general use.
+    conv_dtype = "float32" if dtype == "float16" else dtype
+    b_np = tvm.topi.testing.conv2d_nhwc_python(
+        a_np.astype(conv_dtype), dw_np.astype(conv_dtype), stride, padding
+    ).astype(dtype)
     return a_np, w_np, b_np
 
 
-def test_conv2d_nhwc_gemm_fp32(device, ref_data, dtype, stride, padding, dilation):
+def get_tolerance(dtype, w_np, b_np):
+    if dtype == "float16":
+        # A summation in float16 with a single accumulator very
+        # quickly runs into large rounding errors.
+        # This tolerance is necessary to ensure no false negatives,
+        # but it may introduce false positives, depending on schedule behaviour.
+        num_values_summed = w_np.shape[0] * w_np.shape[1] * w_np.shape[2]
+        next_float_gap_size = np.nextafter(b_np.max(), np.inf, dtype=b_np.dtype) - b_np.max()
+        tol = {"rtol": 1e-5, "atol": num_values_summed * next_float_gap_size / 2}
+    else:
+        tol = {"rtol": 1e-5, "atol": 1e-7}
+
+    return tol
+
+
+def test_conv2d_nhwc_gemm(device, ref_data, dtype, stride, padding, dilation):
     a_np, w_np, b_np = ref_data
 
     A = te.placeholder(a_np.shape, name="A", dtype=dtype)
@@ -130,14 +152,21 @@ def test_conv2d_nhwc_gemm_fp32(device, ref_data, dtype, stride, padding, dilatio
 
         # Run only on AArch64 devices
         # Do not run SVE schedules on non-SVE devices
-        build_only = platform.machine() != "aarch64" or (
-            target.features.has_sve and not tvm.testing.requires_aarch64_sve.run_time_check()
+        build_only = (
+            platform.machine() != "aarch64"
+            or (target.features.has_sve and not tvm.testing.requires_aarch64_sve.run_time_check())
+            or (
+                dtype == "float16"
+                and target.features.has_fp16_simd
+                and not tvm.testing.requires_arm_fp16.run_time_check()
+            )
         )
         if build_only:
             return
 
         func(a, w, b)
-    tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
+    tol = get_tolerance(dtype, w_np, b_np)
+    tvm.testing.assert_allclose(b.numpy(), b_np, rtol=tol["rtol"], atol=tol["atol"])
 
 
 def test_conv2d_nhwc_hwio(target, dev, ref_data, dtype, stride, padding, dilation):
@@ -155,7 +184,8 @@ def test_conv2d_nhwc_hwio(target, dev, ref_data, dtype, stride, padding, dilatio
     b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
     func = tvm.build(s, [A, W, B], target)
     func(a, w, b)
-    tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
+    tol = get_tolerance(dtype, w_np, b_np)
+    tvm.testing.assert_allclose(b.numpy(), b_np, rtol=tol["rtol"], atol=tol["atol"])
 
 
 def test_conv2d_nhwc_ohwi(ref_data, dtype, stride, padding, dilation):
@@ -184,7 +214,8 @@ def test_conv2d_nhwc_ohwi(ref_data, dtype, stride, padding, dilation):
     b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
     func = tvm.build(s, [A, W, B], target)
     func(a, w, b)
-    tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
+    tol = get_tolerance(dtype, w_np_hwio, b_np)
+    tvm.testing.assert_allclose(b.numpy(), b_np, rtol=tol["rtol"], atol=tol["atol"])
 
 
 if __name__ == "__main__":