diff --git a/include/tvm/tir/op.h b/include/tvm/tir/op.h
index 515060595f0e7..19c2cf7b1b9e8 100644
--- a/include/tvm/tir/op.h
+++ b/include/tvm/tir/op.h
@@ -561,7 +561,7 @@ TVM_DLL PrimExpr LargeUIntImm(DataType dtype, int64_t low, int64_t high);
  * \param s integer shift
  * \return The constructed expression.
  */
-TVM_DLL PrimExpr fixed_point_multiply(PrimExpr x, PrimExpr m, PrimExpr s);
+TVM_DLL PrimExpr fixed_point_multiply(PrimExpr x, PrimExpr y, int32_t n);
 
 // Intrinsic operators
 #define TVM_DECLARE_INTRIN_UNARY(OpName)           \
diff --git a/python/tvm/relay/op/_tensor.py b/python/tvm/relay/op/_tensor.py
index 1db4e08bed259..f272bafa06b0d 100644
--- a/python/tvm/relay/op/_tensor.py
+++ b/python/tvm/relay/op/_tensor.py
@@ -20,8 +20,9 @@
 from tvm.te.hybrid import script
 import topi
 
+from . import strategy
 from .op import register_compute, register_shape_func
-from .op import register_broadcast_schedule, register_injective_schedule
+from .op import register_broadcast_schedule, register_injective_schedule, register_strategy
 from .op import register_pattern, OpPattern
 
 
@@ -131,13 +132,9 @@ def clip_compute(attrs, inputs, output_type):
 
 register_injective_schedule("clip")
 
-# fixed point multiply
-@register_compute("fixed_point_multiply")
-def fixed_point_multiply_compute(attrs, inputs, output_type):
-    assert len(inputs) == 1
-    return [topi.fixed_point_multiply(inputs[0], attrs.multiplier, attrs.shift)]
 
-register_injective_schedule("fixed_point_multiply")
+# fixed point multiply
+register_strategy("fixed_point_multiply", strategy.fixed_point_multiply_strategy)
 
 # full
 @script
diff --git a/python/tvm/relay/op/strategy/arm_cpu.py b/python/tvm/relay/op/strategy/arm_cpu.py
index d682aad63bec8..027b72e5be0a0 100644
--- a/python/tvm/relay/op/strategy/arm_cpu.py
+++ b/python/tvm/relay/op/strategy/arm_cpu.py
@@ -37,7 +37,7 @@ def schedule_concatenate_arm_cpu(_, outs, target):
     """schedule concatenate for arm cpu"""
     with target:
         return topi.arm_cpu.schedule_concatenate(outs)
-
+    
 @conv2d_strategy.register(["arm_cpu", "micro_dev"])
 def conv2d_strategy_arm_cpu(attrs, inputs, out_type, target):
     """conv2d arm cpu strategy"""
@@ -332,3 +332,13 @@ def schedule_bitserial_dense_arm_cpu(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.arm_cpu.schedule_bitserial_dense),
         name="bitserial_dense.arm_cpu")
     return strategy
+
+@fixed_point_multiply_strategy.register("arm_cpu")
+def schedule_fixed_point_multiply(attrs, inputs, out_type, target):
+    """bitserial_dense arm cpu strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_fixed_point_multiply(topi.arm_cpu.fixed_point_multiply),
+        wrap_topi_schedule(topi.arm_cpu.schedule_fixed_point_multiply),
+        name="fixed_point_multiply.arm_cpu")
+    return strategy
diff --git a/python/tvm/relay/op/strategy/generic.py b/python/tvm/relay/op/strategy/generic.py
index 632445b71bd2f..93648784578ca 100644
--- a/python/tvm/relay/op/strategy/generic.py
+++ b/python/tvm/relay/op/strategy/generic.py
@@ -124,6 +124,23 @@ def softmax_strategy(attrs, inputs, out_type, target):
         name="softmax.generic")
     return strategy
 
+# fixed_point_multiply
+def wrap_compute_fixed_point_multiply(topi_compute):
+    """Wrap softmax topi compute"""
+    def _compute_fixed_point_multiply(attrs, inputs, out_type):
+        return [topi_compute(inputs[0], attrs.multiplier, attrs.shift)]
+    return _compute_fixed_point_multiply
+
+@override_native_generic_func("fixed_point_multiply_strategy")
+def fixed_point_multiply_strategy(attrs, inputs, out_type, target):
+    """fixed_point_multiply_strategy generic strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_fixed_point_multiply(topi.math.fixed_point_multiply),
+        wrap_topi_schedule(topi.generic.schedule_injective),
+        name="fixed_point_multiply.generic")
+    return strategy
+
 # log_softmax
 @generic_func
 def schedule_log_softmax(attrs, outs, target):
diff --git a/src/target/intrin_rule.cc b/src/target/intrin_rule.cc
index fa2eef3f8f2a1..d6f580191d27c 100644
--- a/src/target/intrin_rule.cc
+++ b/src/target/intrin_rule.cc
@@ -123,42 +123,22 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.fixed_point_multiply")
       const tir::CallNode* call = e.as<tir::CallNode>();
       CHECK(call != nullptr);
 
-      PrimExpr tensor = call->args[0];
-      PrimExpr fixed_point_multiplier = call->args[1];
-      PrimExpr shift = call->args[2];
+      PrimExpr x = call->args[0];
+      PrimExpr y = call->args[1];
+      PrimExpr n = call->args[2];
 
       // Only int32 types are supported (any number of lanes is allowed)
-      CHECK(tensor.dtype().code() == DLDataTypeCode::kDLInt && tensor.dtype().bits() == 32);
-      CHECK(fixed_point_multiplier.dtype().code() == DLDataTypeCode::kDLInt &&
-            fixed_point_multiplier.dtype().bits() == 32);
-      CHECK(shift.dtype().code() == DLDataTypeCode::kDLInt && shift.dtype().bits() == 32);
+      CHECK(x.dtype().code() == DLDataTypeCode::kDLInt && x.dtype().bits() == 32);
+      CHECK(y.dtype().code() == DLDataTypeCode::kDLInt && y.dtype().bits() == 32);
 
-      DataType hp_dtype = DataType::Int(64, tensor.dtype().lanes());
-      DataType lp_dtype = DataType::Int(32, tensor.dtype().lanes());
+      DataType hp_dtype = DataType::Int(64, x.dtype().lanes());
+      DataType lp_dtype = DataType::Int(32, x.dtype().lanes());
+      PrimExpr K = (make_const(hp_dtype, 1) << (n - 1));
 
-      // 1) Calculating the integer multiplier and integer shift
-      PrimExpr zero = make_const(shift.dtype(), 0);
-      PrimExpr left_shift = tir::Select((shift > zero), shift, zero);
-      PrimExpr right_shift = tir::Select(shift > zero, zero, -shift);
-
-      // 2) Multiply the integer multiplier
-      tensor = tir::Select(left_shift != zero, tensor << cast(hp_dtype, left_shift),
-                           cast(hp_dtype, tensor));
-
-      // 3) Perform the multiplication in higher precision.
-      tensor = tensor * fixed_point_multiplier;
-
-      // 4) Find the rounding scalar
-      PrimExpr total_right_shift = right_shift + 31;
-      PrimExpr pos_rounding_value = (make_const(hp_dtype, 1) << (total_right_shift - 1));
-
-      tensor = tensor + pos_rounding_value;
-
-      // 5) Simply right shift the result to get the final output.
-      tensor = tensor >> total_right_shift;
-
-      // 6) The fixed point multiplication keeps the value in int32 range. Casting back to int32.
-      *rv = cast(lp_dtype, tensor);
+      x = cast(hp_dtype, x) * cast(hp_dtype, y);
+      x = x + K;
+      x = x >> n;
+      *rv = cast(lp_dtype, x);
     });
 
 }  // namespace intrin
diff --git a/src/tir/op/op.cc b/src/tir/op/op.cc
index aec41001bbf28..7c8b0a7afb5f0 100644
--- a/src/tir/op/op.cc
+++ b/src/tir/op/op.cc
@@ -91,8 +91,8 @@ PrimExpr LargeUIntImm(DataType t, int64_t low, int64_t high) {
 }
 
 // fixed_point_multiply
-PrimExpr fixed_point_multiply(PrimExpr x, PrimExpr m, PrimExpr s) {
-  return tir::Call(x.dtype(), tir::builtin::fixed_point_multiply(), {x, m, s});
+PrimExpr fixed_point_multiply(PrimExpr x, PrimExpr y, int32_t n) {
+  return tir::Call(x.dtype(), tir::builtin::fixed_point_multiply(), {x, y, make_const(DataType::UInt(32), n)});
 }
 
 // The public function with a quick checking path.
diff --git a/tests/python/relay/test_op_qnn_requantize.py b/tests/python/relay/test_op_qnn_requantize.py
index fb52b30305825..d671a1bf88d1a 100644
--- a/tests/python/relay/test_op_qnn_requantize.py
+++ b/tests/python/relay/test_op_qnn_requantize.py
@@ -90,7 +90,9 @@ def test_downscale():
         # Try positive values
         # 8 corresponds to 0.5, resulting in 1
         golden_data = np.arange(0, 32, 1).astype('int32')
+        print(golden_data)
         golden_output = np.repeat([0, 1, 2], [8, 16, 8])
+        print(golden_output)
         verify(mod, (golden_data, golden_output))
 
         # Try negative values
diff --git a/topi/python/topi/arm_cpu/__init__.py b/topi/python/topi/arm_cpu/__init__.py
index e121fbc7ec6d0..4eb8f0ff7c637 100644
--- a/topi/python/topi/arm_cpu/__init__.py
+++ b/topi/python/topi/arm_cpu/__init__.py
@@ -26,3 +26,4 @@
 from .bitserial_dense import *
 from .injective import *
 from . import cortex_m7
+from .math import fixed_point_multiply
diff --git a/topi/python/topi/arm_cpu/injective.py b/topi/python/topi/arm_cpu/injective.py
index 54306deadd85f..2d4817c859530 100644
--- a/topi/python/topi/arm_cpu/injective.py
+++ b/topi/python/topi/arm_cpu/injective.py
@@ -76,6 +76,37 @@ def schedule_injective(outs):
         schedule_injective_from_existing(s, x)
     return s
 
+def schedule_fixed_point_multiply(outs):
+    """ARM CPU schedule for injective op.
+
+    Parameters
+    ----------
+    outs: Array of Tensor
+          The computation graph description of injective in the format
+          of an array of tensors.
+
+    Returns
+    -------
+    sch: Schedule
+        The computation schedule for the op.
+    """
+    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+    s = te.create_schedule([x.op for x in outs])
+    x = outs[0]
+    ins = x.op.input_tensors
+    dtype = ins[0].dtype if len(ins) > 0 else x.dtype
+    max_vlen = 4 if dtype == 'int32' else 8
+
+    if list(s[x].op.axis):
+        # do not vectorize for broadcast
+        (io, ii) = s[x].split(list(s[x].op.axis)[-1], max_vlen)
+        s[x].vectorize(ii)
+    tvm.te.schedule.AutoInlineInjective(s)
+
+    if not is_empty_shape(x.shape):
+        schedule_injective_from_existing(s, x)
+    return s
+
 def schedule_concatenate(outs):
     """Schedule for concatenate op.
 
diff --git a/topi/python/topi/arm_cpu/tensor_intrin.py b/topi/python/topi/arm_cpu/tensor_intrin.py
index fa004820d6c31..00caac6559eaf 100644
--- a/topi/python/topi/arm_cpu/tensor_intrin.py
+++ b/topi/python/topi/arm_cpu/tensor_intrin.py
@@ -20,6 +20,7 @@
 import tvm
 from tvm import te
 from tvm.contrib import util, clang
+from tvm.ir import Array, Op
 
 def gemv_quantized_impl(M, N, data_type='uint8'):
     """ Assembly implementation of a blocked gemv. Given
@@ -452,7 +453,7 @@ def _instr(index):
         C.op, _intrin_func, binds={data:a_buffer, kernel:b_buffer},
         default_buffer_params=buffer_params)
 
-def _fixed_point_multiply_arm(op):
+def fixed_point_multiply_arm_rule(op):
     """
     Implementation of fixed point multiplication through arm
     intrinsics sqrdmulh and srshl
@@ -460,10 +461,14 @@ def _fixed_point_multiply_arm(op):
     x = op.args[0]
     multiplier = op.args[1]
     shift = op.args[2]
-
-    # Don't use this intrinsic if we don't have a int32x4 vector
+    
+    # Don't use AArch64 intrinsics if we don't have a int32x4 vector
     if x.dtype != "int32x4":
-        return op
+        left_shift = tvm.tir.if_then_else(shift > 0, shift, 0)
+        right_shift = tvm.tir.if_then_else(shift > 0, 0, -shift)
+        x = tvm.tir.if_then_else(left_shift > 0, x << left_shift, x)
+        mulq =  tvm.tir.fixed_point_multiply(x, multiplier, 31)
+        return mulq >> right_shift
 
     # Case 1, shift is negative
     sqrdmulh = tvm.tir.call_llvm_intrin(op.dtype,
@@ -477,7 +482,7 @@ def _fixed_point_multiply_arm(op):
     out_1 = tvm.tir.call_llvm_intrin(op.dtype,
                                      'llvm.aarch64.neon.srshl',
                                      tvm.tir.const(2, 'uint32'),
-                                     sqrdmulh,
+                                     fixed_up_x,
                                      shift)
 
     # Case 2, shift is positive
@@ -491,6 +496,16 @@ def _fixed_point_multiply_arm(op):
     # Select depending on the shift
     return tvm.tir.Select(shift < 0, out_1, out_2)
 
+
+def fixed_point_multiply_arm(x, m, s):
+    """customized log intrinsic function"""
+    return tvm.tir.call_intrin(x.dtype, "tir.fixed_point_multiply_arm", x, m, s)
+
+
 tvm.target.intrin.register_intrin_rule("llvm.aarch64",
-                                       "fixed_point_multiply",
-                                       _fixed_point_multiply_arm, override=True)
+                                       "fixed_point_multiply_arm",
+                                       fixed_point_multiply_arm_rule, override=False)
+
+tvm.ir.register_op_attr("tir.fixed_point_multiply_arm", "TCallEffectKind", tvm.tir.CallEffectKind.Pure)
+tvm.ir.register_op_attr("tir.fixed_point_multiply_arm", "TVectorizable", True)
+
diff --git a/topi/python/topi/math.py b/topi/python/topi/math.py
index 475f8074fb353..c6aa9e986be2e 100644
--- a/topi/python/topi/math.py
+++ b/topi/python/topi/math.py
@@ -612,7 +612,6 @@ def _compute(*indices):
         return tvm.te.max(tvm.te.min(value, const_max), const_min)
     return te.compute(x.shape, _compute)
 
-@tvm.te.tag_scope(tag=tag.ELEMWISE)
 def fixed_point_multiply(x, multiplier, shift):
     """
 
@@ -628,15 +627,20 @@ def fixed_point_multiply(x, multiplier, shift):
     y : tvm.te.Tensor
         The result.
     """
+    left_shift = shift if shift > 0 else 0
+    right_shift = 0 if shift > 0 else -shift
+    
     def _compute(*indices):
-        value = x(*indices)
-        m = tvm.tir.const(multiplier, x.dtype)
-        s = tvm.tir.const(shift, x.dtype)
-        return tvm.tir.fixed_point_multiply(value, m, s)
+        val = x(*indices)
+        val = tvm.tir.if_then_else(left_shift > 0, val << left_shift, val)
+        mulq =  tvm.tir.fixed_point_multiply(val, multiplier, 31)
+        nudge = (1 << (right_shift-1))
+        return (mulq+nudge) >> right_shift
 
     assert x.dtype == "int32", "input tensor type needs to be int32"
     return te.compute(x.shape, _compute)
 
+
 def cast(x, dtype):
     """Cast input to specified data type.