diff --git a/verif/tosa_error_if.py b/verif/tosa_error_if.py
index c28591d..6656645 100644
--- a/verif/tosa_error_if.py
+++ b/verif/tosa_error_if.py
@@ -30,4 +30,6 @@
     BatchMismatch = "BatchMismatch"
     ChannelMismatch = "ChannelMismatch"
     RankMismatch = "RankMismatch"
+    InputZeroPointNotZero = "InputZeroPointNotZero"
+    OutputZeroPointNotZero = "OutputZeroPointNotZero"
 
diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py
index f5f7fff..2478331 100644
--- a/verif/tosa_test_gen.py
+++ b/verif/tosa_test_gen.py
@@ -56,23 +56,38 @@
         pass
 
     @staticmethod
-    def getQinfo(testGen, dtype):
+    def getQinfo(testGen, dtype, error_name=None):
+
         if dtype == DType.INT8:
             return testGen.randInt(-128, 128)
-        if dtype == DType.UINT8:
+        elif dtype == DType.UINT8:
             return testGen.randInt(0, 256)
+        elif error_name in [ErrorIf.InputZeroPointNotZero, ErrorIf.OutputZeroPointNotZero]:
+            zero_point = testGen.randInt(-128, 128)
+            if zero_point == 0:
+                zero_point = 1
+            return zero_point
         return 0
 
     @staticmethod
-    def qgUnary(testGen, op, dtype):
+    def qgUnary(testGen, op, dtype, error_name=None):
         qinfo = ts.TosaSerializerQuantInfo()
-        qinfo.UnaryQuantInfo(
-            TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype)
-        )
+        if error_name == ErrorIf.InputZeroPointNotZero:
+            qinfo.UnaryQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype, error_name), TosaQuantGen.getQinfo(testGen, dtype)
+            )
+        elif error_name == ErrorIf.OutputZeroPointNotZero:
+            qinfo.UnaryQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype, error_name)
+            )
+        else:
+            qinfo.UnaryQuantInfo(
+                TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype)
+            )
         return qinfo
 
     @staticmethod
-    def qgConv(testGen, op, dtype_or_dtypeList):
+    def qgConv(testGen, op, dtype_or_dtypeList, error_name=None):
         qinfo = ts.TosaSerializerQuantInfo()
         if isinstance(dtype_or_dtypeList, list):
             # a list of [input, weights, accumulator] dtypes
@@ -86,7 +101,7 @@
         return qinfo
 
     @staticmethod
-    def qgMatmul(testGen, op, dtype):
+    def qgMatmul(testGen, op, dtype, error_name=None):
         qinfo = ts.TosaSerializerQuantInfo()
         qinfo.MatMulQuantInfo(
             TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype)
@@ -94,7 +109,7 @@
         return qinfo
 
     @staticmethod
-    def qgPad(testGen, op, dtype):
+    def qgPad(testGen, op, dtype, error_name=None):
         qinfo = ts.TosaSerializerQuantInfo()
         qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype))
         return qinfo
@@ -1645,6 +1660,61 @@
         }
         return info_dict
 
+    @staticmethod
+    def evInputZeroPointNotZero(check=False, **kwargs):
+        error_name = ErrorIf.InputZeroPointNotZero
+        param_reqs = {
+            "rank": None,
+            "dtype": [DType.INT16, DType.INT32, DType.FLOAT],
+            "shape": None
+            }
+        error_result = False
+        error_reason = "Input DType not INT8 and zero point not 0"
+
+        if check:
+            input_dtype = kwargs['input_dtype']
+            # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp
+            qinfo = kwargs['qinfo'].ints
+            input_zero_point = qinfo[0][1]
+            if input_dtype not in [DType.INT8, DType.UINT8] and input_zero_point != 0:
+                error_result = True
+
+        info_dict = {
+            "error_name": error_name,
+            "error_result": error_result,
+            "error_reason": error_reason,
+            "param_reqs": param_reqs
+        }
+        return info_dict
+
+
+    @staticmethod
+    def evOutputZeroPointNotZero(check=False, **kwargs):
+        error_name = ErrorIf.OutputZeroPointNotZero
+        param_reqs = {
+            "rank": None,
+            "dtype": [DType.INT16, DType.INT32, DType.FLOAT],
+            "shape": None
+            }
+        error_result = False
+        error_reason = "Output DType not INT8 and zero point not 0"
+
+        if check:
+            output_dtype = kwargs['output_dtype']
+            # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp
+            qinfo = kwargs['qinfo'].ints
+            output_zero_point = qinfo[1][1]
+            if output_dtype not in [DType.INT8, DType.UINT8] and output_zero_point != 0:
+                error_result = True
+
+        info_dict = {
+            "error_name": error_name,
+            "error_result": error_result,
+            "error_reason": error_reason,
+            "param_reqs": param_reqs
+        }
+        return info_dict
+
 
 class TosaInvalidValidator:
 
@@ -1949,13 +2019,47 @@
     # The build_fcn_arg_list is expanded and passed to the operator test
     # build function
 
-    def build_unary(self, op, a, qinfo=None):
-        result_tens = OutputShaper.unaryOp(self.ser, a)
+    def build_unary(self, op, a, validator_fcns=None, error_name=None, qinfo=None):
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
+
         # build_placeholder returns an int, ABS/other ops does not
         if isinstance(op, int):
-            self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo)
-        else:
-            self.ser.addOperator(op['op'], [a.name], [result_tens.name], None, qinfo)
+            self.ser.addOperator(op, a.name, result_tens.name, None, qinfo)
+            return result_tens
+        elif op['op'] == Op.IDENTITY:
+            self.ser.addOperator(op['op'], a.name, result_tens.name, None, qinfo)
+            return result_tens
+
+        # Ensure new output type has correct qinfo
+        if error_name == ErrorIf.WrongOutputType:
+            if result_tens.dtype not in [DType.INT8, DType.UINT8]:
+                qinfo = ts.TosaSerializerQuantInfo()
+                qinfo.UnaryQuantInfo(
+                TosaQuantGen.getQinfo(self, a.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype)
+                )
+
+        # Invalidate Input/Output list for error if checks.
+        input_list = [a.name]
+        output_list = [result_tens.name]
+        pCount, cCount = op["operands"]
+        num_operands = pCount + cCount
+        input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list)
+
+        TosaErrorValidator.evValidateErrorIfs(
+            self.ser,
+            validator_fcns,
+            error_name,
+            op=op,
+            input_dtype=a.dtype,
+            output_dtype=result_tens.dtype,
+            qinfo = qinfo,
+            result_tensor = result_tens,
+            input_list=input_list,
+            output_list=output_list,
+            num_operands=num_operands,
+        )
+
+        self.ser.addOperator(op['op'], input_list, output_list, None, qinfo)
         return result_tens
 
     def build_binary_broadcast(self, op, a, b, validator_fcns, error_name=None):
@@ -2139,7 +2243,7 @@
         return result_tens
 
     def build_clamp(self, op, a):
-        result_tens = OutputShaper.unaryOp(self.ser, a)
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None)
 
         attr = ts.TosaSerializerAttribute()
         v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)]
@@ -2153,7 +2257,7 @@
         return result_tens
 
     def build_leaky_relu(self, op, a):
-        result_tens = OutputShaper.unaryOp(self.ser, a)
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None)
         attr = ts.TosaSerializerAttribute()
 
         attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT))
@@ -2163,18 +2267,18 @@
 
     # Needs an additional type/input
     def build_prelu(self, op, a):
-        result_tens = OutputShaper.unaryOp(self.ser, a)
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None)
 
         self.ser.addOperator(op['op'], [a.name], [result_tens.name])
         return result_tens
 
     def build_sigmoid(self, op, a):
-        result_tens = OutputShaper.unaryOp(self.ser, a)
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None)
         self.ser.addOperator(op['op'], [a.name], [result_tens.name])
         return result_tens
 
     def build_tanh(self, op, a):
-        result_tens = OutputShaper.unaryOp(self.ser, a)
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None)
         self.ser.addOperator(op['op'], [a.name], [result_tens.name])
         return result_tens
 
@@ -2220,7 +2324,7 @@
         return result_tens
 
     def build_reverse(self, op, a, axis):
-        result_tens = OutputShaper.unaryOp(self.ser, a)
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None)
 
         attr = ts.TosaSerializerAttribute()
         attr.AxisAttribute(axis)
@@ -2365,9 +2469,8 @@
         return result_tens
 
     def build_identityn(self, op, val, val2):
-
-        result_tens = OutputShaper.unaryOp(self.ser, val)
-        result_tens2 = OutputShaper.unaryOp(self.ser, val2)
+        result_tens = OutputShaper.unaryOp(self.ser, self.rng, val, None)
+        result_tens2 = OutputShaper.unaryOp(self.ser, self.rng, val2, None)
         self.ser.addOperator(
             op, [val.name, val2.name], [result_tens.name, result_tens2.name]
         )
@@ -2784,7 +2887,7 @@
         tens = self.generate_tensors(op, dtypeList, shapeList, testArgs, error_name)
 
         if qgen is not None:
-            qinfo = qgen(self, op, dtype_or_dtypeList)
+            qinfo = qgen(self, op, dtype_or_dtypeList, error_name)
         else:
             qinfo = None
 
@@ -2796,7 +2899,7 @@
                     resultName = build_fcn(self, op, *tens, *testArgs)
             else:
                 if qinfo is not None:
-                    resultName = build_fcn(self, op, *tens, *testArgs, qinfo, error_if_validators, error_name)
+                    resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name, qinfo)
                 else:
                     resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name)
         except TypeError as e:
@@ -3388,48 +3491,64 @@
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_FI32,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "bitwise_not": {
             "op": Op.BITWISE_NOT,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_INT,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "ceil": {
             "op": Op.CEIL,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_FP,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "clz": {
             "op": Op.CLZ,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": [DType.INT32],
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "exp": {
             "op": Op.EXP,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_FP,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "floor": {
             "op": Op.FLOOR,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_FP,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "log": {
             "op": Op.LOG,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_FP,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "logical_not": {
             "op": Op.LOGICAL_NOT,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_BOOL,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "negate": {
             "op": Op.NEGATE,
@@ -3437,18 +3556,25 @@
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "qgen": TosaQuantGen.qgUnary,
             "types": TYPE_INT_FP,
+            "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero,
+            TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList,
+            TosaErrorValidator.evWrongOutputList)
         },
         "reciprocal": {
             "op": Op.RECIPROCAL,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_FP,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         "rsqrt": {
             "op": Op.RSQRT,
             "operands": (1, 0),
             "build_fcn": (build_unary, TosaTensorGen.tgBasic, None),
             "types": TYPE_FP,
+            "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType,
+            TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList)
         },
         # Elementwise Ternary operators
         "select": {
@@ -3703,8 +3829,15 @@
         return ser.addOutput(shape, a.dtype)
 
     @staticmethod
-    def unaryOp(ser, a):
-        return ser.addOutput(a.shape, a.dtype)
+    def unaryOp(ser, rng, a, error_name=None):
+        if error_name == ErrorIf.WrongOutputType:
+            all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT]
+            wrong_dtypes = list(set(all_dtypes) - set([a.dtype]))
+            outputDType = rng.choice(wrong_dtypes)
+        else:
+            outputDType = a.dtype
+
+        return ser.addOutput(a.shape, outputDType)
 
     @staticmethod
     def selectOp(ser, cond, a, b):