diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 4e15b06..fed91f6 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2022, ARM Limited.
+# Copyright (c) 2021-2023, ARM Limited.
 # SPDX-License-Identifier: Apache-2.0
 import itertools
 import math
@@ -417,6 +417,41 @@
         return [ifm_shape, filter_shape, bias_shape]
 
     @staticmethod
+    def tgRFFT2d(testGen, op, rank, error_name=None):
+        pl, const = op["operands"]
+
+        if error_name != ErrorIf.WrongRank:
+            assert rank == 3
+        assert pl == 1 and const == 0
+
+        # IFM dimensions are NHW
+        ifm_shape = testGen.makeShape(rank)
+
+        # Select nearest lower power of two from input height and width
+        ifm_shape[1] = 2 ** int(math.log(ifm_shape[1], 2))
+        ifm_shape[2] = 2 ** int(math.log(ifm_shape[2], 2))
+
+        # Constrict the overall size of the shape when creating ERROR_IF tests
+        if error_name:
+            ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(ifm_shape)
+
+        # Generate an invalid kernel that is not a power of two
+        if error_name == ErrorIf.KernelNotPowerOfTwo:
+            # We must increment by 2 if current size is 1
+            inc_h = 2 if ifm_shape[1] == 1 else 1
+            inc_w = 2 if ifm_shape[2] == 1 else 1
+            inc_choices = [(inc_h, 0), (0, inc_w), (inc_h, inc_w)]
+            selected_inc = testGen.rng.choice(inc_choices)
+            ifm_shape[1] += selected_inc[0]
+            ifm_shape[2] += selected_inc[1]
+
+        # Constrict the batch size
+        if testGen.args.max_batch_size:
+            ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1
+
+        return [ifm_shape]
+
+    @staticmethod
     def tgFullyConnected(testGen, op, rank, error_name=None):
         pl, const = op["operands"]
 
diff --git a/verif/generator/tosa_error_if.py b/verif/generator/tosa_error_if.py
index c9d35c7..40c5d13 100644
--- a/verif/generator/tosa_error_if.py
+++ b/verif/generator/tosa_error_if.py
@@ -1,5 +1,7 @@
-# Copyright (c) 2021-2022, ARM Limited.
+# Copyright (c) 2021-2023, ARM Limited.
 # SPDX-License-Identifier: Apache-2.0
+import math
+
 import numpy as np
 from generator.tosa_utils import MAX_RESIZE_DIMENSION
 from generator.tosa_utils import product
@@ -76,6 +78,7 @@
     CondIfCondNotMatchingBool = "CondIfCondNotMatchingBool"
     CondIfCondShapeNotSizeOne = "CondIfCondShapeNotSizeOne"
     CondGraphOutputShapeNotSizeOne = "CondGraphOutputShapeNotSizeOne"
+    KernelNotPowerOfTwo = "KernelNotPowerOfTwo"
 
 
 class TosaErrorIfArgGen:
@@ -548,6 +551,10 @@
                 ):
                     error_result = True
 
+            elif op["op"] == Op.RFFT2D:
+                if not all([ty == input_dtype for ty in output_dtype]):
+                    error_result = True
+
             elif op["op"] in {
                 Op.CONV2D,
                 Op.CONV3D,
@@ -665,9 +672,13 @@
         error_reason = "Op output list does not match expected output"
 
         if check:
+            op = kwargs["op"]
             output_list = kwargs["output_list"]
-            # Note this will be incorrect if an operator returns more than one output
-            if len(output_list) != 1:
+            expected_length = 1
+            if op["op"] == Op.RFFT2D:
+                expected_length = 2
+
+            if len(output_list) != expected_length:
                 error_result = True
 
         info_dict = {
@@ -711,7 +722,7 @@
     @staticmethod
     def evBatchMismatch(check=False, **kwargs):
         error_name = ErrorIf.BatchMismatch
-        param_reqs = {"rank": [4, 4], "dtype": None, "shape": None}
+        param_reqs = {"rank": None, "dtype": None, "shape": None}
         error_result = False
         error_reason = "Input batch size not equal to output batch size"
 
@@ -722,12 +733,15 @@
 
         if check:
             input_shape = kwargs["input_shape"]
-            output_shape = kwargs[
-                "result_tensor"
-            ].shape  # Note this is just (N, OH, OW, C)
 
-            if (len(input_shape) in rank_range) and (input_shape[0] != output_shape[0]):
-                error_result = True
+            for output in kwargs["result_tensors"]:
+                output_shape = (
+                    output.shape
+                )  # Note batch is expected to be the first dim
+                if (len(input_shape) in rank_range) and (
+                    input_shape[0] != output_shape[0]
+                ):
+                    error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -751,11 +765,12 @@
 
         if check:
             input_shape = kwargs["input_shape"]
-            output_shape = kwargs[
-                "result_tensor"
-            ].shape  # Note this is just (N, OH, OW, C)
-            if (len(input_shape) in rank_range) and (input_shape[3] != output_shape[3]):
-                error_result = True
+            for output in kwargs["result_tensors"]:
+                output_shape = output.shape  # Note this is just (N, OH, OW, C)
+                if (len(input_shape) in rank_range) and (
+                    input_shape[3] != output_shape[3]
+                ):
+                    error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -1044,13 +1059,15 @@
             input3_shape = (
                 kwargs["input3"].shape if "input3" in kwargs else input2_shape
             )
-            output_shape = kwargs["result_tensor"].shape
-            if (
-                (len(input1_shape) != len(output_shape))
-                or (len(input2_shape) != len(output_shape))
-                or (len(input3_shape) != len(output_shape))
-            ):
-                error_result = True
+
+            for output in kwargs["result_tensors"]:
+                output_shape = output.shape
+                if (
+                    (len(input1_shape) != len(output_shape))
+                    or (len(input2_shape) != len(output_shape))
+                    or (len(input3_shape) != len(output_shape))
+                ):
+                    error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -1074,16 +1091,18 @@
             input3_shape = (
                 kwargs["input3"].shape if "input3" in kwargs else input2_shape
             )
-            output_shape = kwargs["result_tensor"].shape
-            for i in range(
-                min(len(input1_shape), len(input2_shape), len(input3_shape))
-            ):
-                if (
-                    (input1_shape[i] != 1 and input1_shape[i] != output_shape[i])
-                    or (input2_shape[i] != 1 and input2_shape[i] != output_shape[i])
-                    or (input3_shape[i] != 1 and input3_shape[i] != output_shape[i])
+
+            for output in kwargs["result_tensors"]:
+                output_shape = output.shape
+                for i in range(
+                    min(len(input1_shape), len(input2_shape), len(input3_shape))
                 ):
-                    error_result = True
+                    if (
+                        (input1_shape[i] != 1 and input1_shape[i] != output_shape[i])
+                        or (input2_shape[i] != 1 and input2_shape[i] != output_shape[i])
+                        or (input3_shape[i] != 1 and input3_shape[i] != output_shape[i])
+                    ):
+                        error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -2392,6 +2411,30 @@
         }
         return info_dict
 
+    @staticmethod
+    def evKernelNotPowerOfTwo(check=False, **kwargs):
+        error_name = ErrorIf.KernelNotPowerOfTwo
+        param_reqs = {"rank": None, "dtype": None, "shape": None}
+        error_result = False
+        error_reason = "kernel height and/or width not a power of two"
+
+        def is_power_of_two(x):
+            return math.log(x, 2).is_integer()
+
+        if check:
+            shape = kwargs["input_shape"]
+            if len(shape) == 3:
+                valid_kernel = is_power_of_two(shape[1]) and is_power_of_two(shape[2])
+                error_result = not valid_kernel
+
+        info_dict = {
+            "error_name": error_name,
+            "error_result": error_result,
+            "error_reason": error_reason,
+            "param_reqs": param_reqs,
+        }
+        return info_dict
+
 
 class TosaInvalidValidator:
     @staticmethod
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index c29763b..fddf942 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -255,7 +255,7 @@
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
             qinfo=qinfo,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -293,7 +293,7 @@
             input2=b,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -333,7 +333,7 @@
             input2=b,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -378,7 +378,7 @@
             input2=b,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -414,7 +414,7 @@
             input_shape=a.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -448,7 +448,7 @@
             input_shape=a.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -487,7 +487,7 @@
             input_dtype=a.dtype,
             output_shape=result_tens.shape,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -523,7 +523,7 @@
             input_dtype=a.dtype,
             output_shape=result_tens.shape,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -582,7 +582,7 @@
             stride=stride,
             pad=pad,
             qinfo=qinfo,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -938,7 +938,7 @@
             output_shape=result_tens.shape,
             output_dtype=result_tens.dtype,
             qinfo=qinfo,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -980,7 +980,7 @@
             output_shape=result_tens.shape,
             output_dtype=result_tens.dtype,
             qinfo=qinfo,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1016,7 +1016,7 @@
             output_shape=result_tens.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1064,7 +1064,7 @@
             output_shape=result_tens.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1122,7 +1122,7 @@
             output_shape=result_tens.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1153,7 +1153,7 @@
             output_shape=result_tens.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1199,7 +1199,7 @@
             input_dtype=a[0].dtype,
             output_dtype=result_tens.dtype,
             inputs=a,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1250,7 +1250,7 @@
             output_dtype=result_tens.dtype,
             pad=padding,
             qinfo=qinfo,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1283,7 +1283,7 @@
             output_shape=result_tens.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1318,7 +1318,7 @@
             output_shape=result_tens.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1356,7 +1356,7 @@
             perms=perms,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1391,7 +1391,7 @@
             output_dtype=result_tens.dtype,
             start=start,
             size=size,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1425,7 +1425,7 @@
             output_shape=result_tens.shape,
             input_dtype=a.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1474,7 +1474,7 @@
             output_shape=result_tens.shape,
             input_dtype=values.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1519,7 +1519,7 @@
             output_shape=result_tens.shape,
             input_dtype=values_in.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1580,7 +1580,7 @@
             border=border,
             input_list=input_list,
             output_list=output_list,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             num_operands=num_operands,
         ):
             return None
@@ -1628,7 +1628,7 @@
             output_shape=result_tens.shape,
             input_dtype=val.dtype,
             output_dtype=result_tens.dtype,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -1774,7 +1774,7 @@
             double_round=double_round,
             input_list=input_list,
             output_list=output_list,
-            result_tensor=result_tens,
+            result_tensors=[result_tens],
             num_operands=num_operands,
         ):
             return None
@@ -2083,6 +2083,38 @@
 
         return acc_out
 
+    def build_rfft2d(self, op, val, validator_fcns=None, error_name=None):
+        results = OutputShaper.rfft2dOp(self.ser, self.rng, val, error_name)
+
+        input_names = [val.name]
+        pCount, cCount = op["operands"]
+        num_operands = pCount + cCount
+
+        output_names = [res.name for res in results]
+        output_dtypes = [res.dtype for res in results]
+
+        input_names, output_names = TosaErrorIfArgGen.eiInvalidateInputOutputList(
+            self, error_name, input_names, output_names
+        )
+
+        if not TosaErrorValidator.evValidateErrorIfs(
+            self.ser,
+            validator_fcns,
+            error_name,
+            op=op,
+            input_shape=val.shape,
+            input_dtype=val.dtype,
+            output_dtype=output_dtypes,
+            result_tensors=results,
+            input_list=input_names,
+            output_list=output_names,
+            num_operands=num_operands,
+        ):
+            return None
+
+        self.ser.addOperator(op["op"], input_names, output_names)
+        return results
+
     def create_filter_lists(
         self, op, shapeFilter, rankFilter, dtypeFilter, testType, validator=None
     ):
@@ -3897,6 +3929,27 @@
                 TosaErrorValidator.evCondGraphOutputShapeNotSizeOne,
             ),
         },
+        "rfft2d": {
+            "op": Op.RFFT2D,
+            "operands": (1, 0),
+            "rank": (3, 3),
+            "build_fcn": (
+                build_rfft2d,
+                TosaTensorGen.tgRFFT2d,
+                TosaTensorValuesGen.tvgDefault,
+                TosaArgGen.agNone,
+            ),
+            "types": [DType.FP32],
+            "error_if_validators": (
+                TosaErrorValidator.evWrongInputType,
+                TosaErrorValidator.evWrongOutputType,
+                TosaErrorValidator.evWrongInputList,
+                TosaErrorValidator.evWrongOutputList,
+                TosaErrorValidator.evWrongRank,
+                TosaErrorValidator.evBatchMismatch,
+                TosaErrorValidator.evKernelNotPowerOfTwo,
+            ),
+        },
     }
 
 
@@ -4717,3 +4770,26 @@
             out_dtype = rng.choice(wrong_dtypes)
 
         return ser.addOutput(output_shape, out_dtype)
+
+    @staticmethod
+    def rfft2dOp(serializer, rng, value, error_name=None):
+        outputs = []
+
+        input_shape = value.shape
+        if error_name != ErrorIf.WrongRank:
+            assert len(input_shape) == 3
+
+        output_shape = [*input_shape[:-1], input_shape[-1] // 2 + 1]
+
+        output_dtype = value.dtype
+        if error_name == ErrorIf.WrongOutputType:
+            excludes = [DType.FP32]
+            wrong_dtypes = list(usableDTypes(excludes=excludes))
+            output_dtype = rng.choice(wrong_dtypes)
+        elif error_name == ErrorIf.BatchMismatch:
+            incorrect_batch = input_shape[0] + rng.integers(1, 10)
+            output_shape = [incorrect_batch, *input_shape[1:]]
+
+        outputs.append(serializer.addOutput(output_shape, output_dtype))
+        outputs.append(serializer.addOutput(output_shape, output_dtype))
+        return outputs