diff --git a/src/target/llvm/intrin_rule_hexagon.cc b/src/target/llvm/intrin_rule_hexagon.cc
index c96245e1399c..7c4b38c1d702 100644
--- a/src/target/llvm/intrin_rule_hexagon.cc
+++ b/src/target/llvm/intrin_rule_hexagon.cc
@@ -182,10 +182,18 @@ TVM_REGISTER_OP("tir.sigmoid")
         useqhl = tstring.find("+hvx-qfloat") != std::string::npos;
       }
 
+      PrimExpr MinBound = tir::make_const(x.dtype(), -8);
+      PrimExpr MaxBound = tir::make_const(x.dtype(), 8);
+      const PrimExpr v1 = tir::Max(x, MinBound);
+      const PrimExpr v2 = tir::Min(v1, MaxBound);
+
+      Array<tvm::PrimExpr> new_args = {v2};
+      const tir::Call new_call = tir::Call(call->dtype, call->op, new_args);
+
       // Enable QHL library for FP16 data type
       if (x->dtype.is_float16() && x->dtype.lanes() > 1 && useqhl) {
         std::string tvm_wrapper("tvm_vect_qhmath_hvx_sigmoid_ahf");
-        return TVMExternCall(call, tvm_wrapper);
+        return TVMExternCall(new_call.get(), tvm_wrapper);
       }
 #endif
       PrimExpr one = tir::make_const(x.dtype(), 1);
diff --git a/src/tir/op/op.cc b/src/tir/op/op.cc
index 509badbebb92..3f7f05fe8e64 100644
--- a/src/tir/op/op.cc
+++ b/src/tir/op/op.cc
@@ -866,7 +866,7 @@ TIR_REGISTER_PURE_UNARY_OP("tir.erf");
 
 TIR_REGISTER_PURE_UNARY_OP("tir.tanh").set_attr<TVectorizable>("TVectorizable", true);
 
-TIR_REGISTER_PURE_UNARY_OP("tir.sigmoid");
+TIR_REGISTER_PURE_UNARY_OP("tir.sigmoid").set_attr<TVectorizable>("TVectorizable", true);
 
 TIR_REGISTER_PURE_UNARY_OP("tir.sqrt").set_attr<TVectorizable>("TVectorizable", true);
 
diff --git a/tests/python/contrib/test_hexagon/test_sigmoid.py b/tests/python/contrib/test_hexagon/test_sigmoid.py
new file mode 100644
index 000000000000..9aad35ee76c1
--- /dev/null
+++ b/tests/python/contrib/test_hexagon/test_sigmoid.py
@@ -0,0 +1,117 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+from tvm import te
+from tvm import tir
+from tvm import topi
+from tvm.contrib.hexagon.build import HexagonLauncher
+
+from .infrastructure import allocate_hexagon_array, transform_numpy
+
+
+def sigmoid_compute(Input):
+    return topi.sigmoid(Input)
+
+
+def sigmoid_stir_schedule(Input, Output):
+    sigmoid_func = te.create_prim_func([Input, Output])
+    sch = tir.Schedule(sigmoid_func, debug_mask="all")
+    block = sch.get_block("compute")
+
+    (n,) = sch.get_loops(block)
+    sch.vectorize(n)
+    return sch
+
+
+@tvm.testing.fixture
+def input_np(in_shape, dtype, min_val, max_val):
+    return np.random.uniform(low=min_val, high=max_val, size=in_shape).astype(dtype)
+
+
+@tvm.testing.fixture
+def ref_output_np(input_np):
+    output_np = 1 / (1 + np.exp(-input_np))
+    return output_np
+
+
+class BaseSigmoid:
+    (in_shape, dtype, min_val, max_val,) = tvm.testing.parameters(
+        ((64,), "float16", -8.0, 8.0),
+        ((64,), "float16", -6.0, 7.0),
+        ((64,), "float16", -10.0, 15.0),
+        ((64,), "float16", -10.0, 0.0),
+        ((64,), "float16", 0.0, 10.0),
+    )
+
+
+class TestSigmoid(BaseSigmoid):
+    @tvm.testing.requires_hexagon
+    def test_sigmoid(
+        self,
+        in_shape,
+        dtype,
+        input_np,
+        ref_output_np,
+        target,
+        hexagon_session,
+    ):
+        InputTensor = te.placeholder(in_shape, name="InputTensor", dtype=dtype)
+
+        OutputTensor = sigmoid_compute(InputTensor)
+
+        target_hexagon = tvm.target.hexagon("v69")
+        target = tvm.target.Target(target_hexagon, host=target_hexagon)
+
+        tir_s = sigmoid_stir_schedule(InputTensor, OutputTensor)
+
+        input_data = allocate_hexagon_array(
+            hexagon_session.device,
+            data=input_np,
+        )
+        output_data = allocate_hexagon_array(
+            hexagon_session.device,
+            tensor_shape=ref_output_np.shape,
+            dtype=ref_output_np.dtype,
+        )
+
+        func_name = "sigmoid"
+        with tvm.transform.PassContext(opt_level=3):
+            runtime_module = tvm.build(tir_s.mod, target=target, name=func_name)
+
+        assert "hvx_sigmoid" in runtime_module.get_source("asm")
+        assert "vmin" in runtime_module.get_source("asm")
+        assert "vmax" in runtime_module.get_source("asm")
+        mod = hexagon_session.load_module(runtime_module)
+
+        mod(input_data, output_data)
+        output_np = output_data.numpy()
+
+        tvm.testing.assert_allclose(
+            output_np,
+            ref_output_np,
+            1e-3,
+            1e-3,
+        )
+
+
+if __name__ == "__main__":
+    tvm.testing.main()