diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index fbf5a82..d5d08ba 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -739,9 +739,9 @@
                         "--fp-values-range",
                         "-max,max",
                         "--target-shape",
-                        "1,34,19,27",
+                        "2,34,19,27",
                         "--target-shape",
-                        "1,16,51,7",
+                        "3,16,51,7",
                         "--tensor-dim-range",
                         "1,16",
                         "--allow-pooling-and-conv-oversizes"
@@ -760,6 +760,28 @@
                         "--max-conv-dilation",
                         "1",
                         "--allow-pooling-and-conv-oversizes"
+                    ],
+                    [
+                        "--target-dtype",
+                        "fp32",
+                        "--target-dtype",
+                        "fp16",
+                        "--target-dtype",
+                        "bf16",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--conv-kernel",
+                        "7,7",
+                        "--conv-kernel",
+                        "9,9",
+                        "--target-shape",
+                        "1,127,127,1",
+                        "--target-shape",
+                        "1,256,256,1",
+                        "--target-shape",
+                        "1,1080,720,1",
+                        "--tensor-dim-range",
+                        "1,8"
                     ]
                 ]
             },
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 41ef4df..3a85961 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -339,7 +339,13 @@
         # The filter dimensions are OHWI
         filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]])
 
-        # The bias is OC
+        # The bias is OC or 1 if broadcastable
+        try:
+            if op["broadcastable_bias"]:
+                if rng.choice([True, False]):
+                    ofm_depth = 1
+        except KeyError:
+            pass
         bias_shape = np.asarray([ofm_depth])
 
         return [ifm_shape, filter_shape, bias_shape]
@@ -1868,7 +1874,7 @@
                             else ""
                         )
                         logger.info(
-                            f"Skipping {opName}{shape_info} dot product test as too few calculations {dot_products} < {testGen.TOSA_MI_DOT_PRODUCT_MIN}"
+                            f"Skipping {opName}{shape_info} {gtu.DTYPE_ATTRIBUTES[dtype]['json']} dot product test as too few calculations {dot_products} < {testGen.TOSA_MI_DOT_PRODUCT_MIN}"
                         )
                         continue
                     # KS and acc_type is required by all dot product generators
@@ -1986,10 +1992,6 @@
         # Used by CONV2D, CONV3D and DEPTHWISE_CONV2D
         arg_list = []
 
-        if testGen.args.level8k and error_name is not None:
-            # Don't produce negative large tests
-            return arg_list
-
         # Shape: Batches, (Depth), Height, Width, Channels
         ifm_shape = shapeList[0]
         # Shape: (OFM channels), (KD), KH, KW, IFM channels
@@ -2002,124 +2004,116 @@
                 [DType.BF16] if gtu.dtypeIsFloat(dtypes[0]) else [DType.INT16]
             )
 
-        # Op type checks
-        conv3d = opName.startswith("conv3d")
-        depthwise = opName.startswith("depthwise")
+        # For op type checks
+        op = testGen.TOSA_OP_LIST[opName]
 
         # Check the rank
-        rank = 5 if conv3d else 4
+        rank = 5 if op["op"] == Op.CONV3D else 4
         if error_name != ErrorIf.WrongRank:
             assert len(ifm_shape) == rank
             assert len(filter_shape) == rank
 
         # kernel rank omits channels
         k_rank = rank - 2
-        k_pos = 0 if depthwise else 1
+        k_pos = 0 if op["op"] == Op.DEPTHWISE_CONV2D else 1
         k_shape = tuple(filter_shape[k_pos : (k_pos + k_rank)])
         # compliance size - KS
         k_size = gtu.product(k_shape)
-        if not depthwise:
+        if not op["op"] == Op.DEPTHWISE_CONV2D:
             k_size *= ifm_shape[-1]
 
-        if not testGen.args.level8k:
-            if error_name in (
-                ErrorIf.PadSmallerZero,
-                ErrorIf.StrideSmallerOne,
-                ErrorIf.DilationSmallerOne,
-            ):
-                # Use specific invalid value(s)
-                if error_name == ErrorIf.PadSmallerZero:
-                    # Create negative paddings but with positive opposite paddings
-                    neg_pad = rng.choice(range(-5, 0))
-                    p_vals = [neg_pad, abs(neg_pad)]
-                else:
-                    p_vals = [0, 0]
-                if error_name == ErrorIf.StrideSmallerOne:
-                    # Can't use stride=0, as it is used to derive output shape, as a divisor
-                    s_vals = [rng.choice(range(-5, 0))]
-                else:
-                    s_vals = [1]
-                if error_name == ErrorIf.DilationSmallerOne:
-                    d_vals = [rng.choice(range(-5, 1))]
-                else:
-                    d_vals = [1]
-                p = p_vals * k_rank
-                s = s_vals * k_rank
-                d = d_vals * k_rank
-
-                # Fix values to produce valid error_if
-                for index in range(k_rank):
-                    pad_offset = index * 2
-                    fixed = False
-                    while not fixed:
-                        partial = (
-                            ifm_shape[index + 1]
-                            - 1
-                            + p[pad_offset]
-                            + p[pad_offset + 1]
-                            - (k_shape[index] - 1) * d[index]
-                        )
-                        remainder = partial % s[index]
-                        if partial <= 0:
-                            p[pad_offset + 1] += abs(partial) + 1
-                        elif remainder:
+        def get_conv_output_info(p, s, d, fix_up_padding=False):
+            # Work out remainders and output dimensions with an
+            # option to adjust paddings to create a valid operation
+            nonlocal ifm_shape, k_shape, error_name, k_rank
+            if fix_up_padding:
+                p = list(p)  # Make paddings editable
+            outputs_no_stride = []
+            remainders = []
+            outputs = []
+            for index in range(k_rank):
+                pad_offset = index * 2
+                fixed = False
+                # Fix up pad values to produce valid conv2d
+                while not fixed:
+                    # Output dimension without being adjusted for stride
+                    output_no_stride = (
+                        ifm_shape[index + 1]
+                        - 1
+                        + p[pad_offset]
+                        + p[pad_offset + 1]
+                        - (k_shape[index] - 1) * d[index]
+                    )
+                    # Tensor left over after applying striding
+                    remainder = output_no_stride % s[index]
+                    if not fix_up_padding:
+                        # Just want remainders and outputs
+                        break
+                    if output_no_stride <= 0:
+                        p[pad_offset + 1] += abs(output_no_stride) + 1
+                        continue
+                    if error_name == ErrorIf.ConvOutputShapeNonInteger:
+                        if remainder:
+                            # Conditions to trigger the test
+                            fixed = True
+                        else:
+                            p[pad_offset + 1] += 1
+                    else:
+                        if remainder:
                             # Stride will be negative for StrideSmallerOne
-                            assert remainder < 0
+                            assert remainder > 0 or (
+                                error_name == ErrorIf.StrideSmallerOne and remainder < 0
+                            )
                             p[pad_offset + 1] += abs(remainder)
                         else:
                             fixed = True
-                paddings = {tuple(p)}
-                strides = {tuple(s)}
-                dilations = {tuple(d)}
-                logger.debug(f"agConv: error pad={p} stride={s} dilation={d}")
+                outputs_no_stride.append(output_no_stride)
+                remainders.append(remainder)
+                # Output dimension taking in to account stride
+                outputs.append((output_no_stride // s[index]) + 1)
+
+            if fix_up_padding:
+                p = tuple(p)  # Make the paddings read-only
+                assert min(outputs_no_stride) > 0, "Fix up did not work!"
+            return p, remainders, outputs, outputs_no_stride
+
+        # Only fix up padding for conv2d and float types currently
+        fix_up_padding = gtu.dtypeIsFloat(dtypes[0]) and op["op"] == Op.CONV2D
+        # Allow any size of output dimension
+        max_dim_size = None
+        # Include all tests by default
+        sparsity = 1
+
+        # Work out padding, strides and dilation ranges depending on
+        # error and arguments
+        if error_name in (
+            ErrorIf.PadSmallerZero,
+            ErrorIf.StrideSmallerOne,
+            ErrorIf.DilationSmallerOne,
+        ):
+            # Use specific invalid value(s)
+            if error_name == ErrorIf.PadSmallerZero:
+                # Create negative paddings but with positive opposite paddings
+                neg_pad = rng.choice(range(-5, 0))
+                p_vals = [neg_pad, abs(neg_pad)]
             else:
-                # Generate comprehensive argument lists
-                p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)]
-                paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))}
-                # Stride must be greater than 1 to force non-integer error
-                startStride = (
-                    1 if error_name != ErrorIf.ConvOutputShapeNonInteger else 2
-                )
-                s_vals = [
-                    x for x in range(startStride, testGen.args.max_conv_stride + 1)
-                ]
-                d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)]
-
-                strides = {x for x in itertools.product(*([s_vals] * k_rank))}
-                dilations = {x for x in itertools.product(*([d_vals] * k_rank))}
-
-            if not error_name and testGen.args.oversize:
-                # add some oversize argument values
-                if max(ifm_shape) < 64:
-                    bigPadding = 9
-                    paddings.update(
-                        {
-                            x
-                            for x in itertools.product(
-                                *([[0, bigPadding]] * (k_rank * 2))
-                            )
-                        }
-                    )
-                bigStride = 8
-                strides.update(
-                    {x for x in itertools.product(*([[1, bigStride]] * k_rank))}
-                )
-                bigDilation = 7
-                dilations.update(
-                    {x for x in itertools.product(*([[1, bigDilation]] * k_rank))}
-                )
-            max_dim_size = None
-
-            if error_name:
-                # Cycle through all error_if tests but we only keep the first few
-                sparsity = 1
+                p_vals = [0, 0]
+            if error_name == ErrorIf.StrideSmallerOne:
+                # Can't use stride=0, as it is used to derive output shape, as a divisor
+                s_vals = [rng.choice(range(-5, 0))]
             else:
-                # There are too many parameter combinations, so generate them sparsely,
-                sparsity_factor = 120
-                sparsity = TosaArgGen._calculate_sparsity(
-                    len(paddings) * len(strides) * len(dilations), sparsity_factor
-                )
-        else:
+                s_vals = [1]
+            if error_name == ErrorIf.DilationSmallerOne:
+                d_vals = [rng.choice(range(-5, 1))]
+            else:
+                d_vals = [1]
+            paddings = {tuple(p_vals) * k_rank}
+            strides = {tuple(s_vals) * k_rank}
+            dilations = {tuple(d_vals) * k_rank}
+
+            fix_up_padding = True  # Need to fix up paddings to be valid
+
+        elif testGen.args.level8k and error_name is None:
             # Only test 8k levels boundaries
             bigStride = testGen.TOSA_8K_LEVEL_MAX_STRIDE
             bigKernel = testGen.TOSA_8K_LEVEL_MAX_KERNEL
@@ -2127,7 +2121,7 @@
 
             dilation_shape = [1] * k_rank
             pad_shape = [0] * k_rank * 2
-            if conv3d:
+            if op["op"] == Op.CONV3D:
                 # Small stride apart from for big kernel (see below) to keep
                 # tensor size/calculation small
                 stride_shape = [1] * k_rank
@@ -2162,43 +2156,66 @@
 
             # Currently allow all combinations that are reasonable size
             sparsity = 1
+        else:
+            # Generate comprehensive argument lists
+            p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)]
+            paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))}
+            # Stride must be greater than 1 to force non-integer error
+            startStride = 1 if error_name != ErrorIf.ConvOutputShapeNonInteger else 2
+            s_vals = [x for x in range(startStride, testGen.args.max_conv_stride + 1)]
+            d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)]
 
+            strides = {x for x in itertools.product(*([s_vals] * k_rank))}
+            dilations = {x for x in itertools.product(*([d_vals] * k_rank))}
+
+            if error_name is None and testGen.args.oversize:
+                # add some oversize argument values
+                if max(ifm_shape) < 64:
+                    bigPadding = 9
+                    paddings.update(
+                        {
+                            x
+                            for x in itertools.product(
+                                *([[0, bigPadding]] * (k_rank * 2))
+                            )
+                        }
+                    )
+                bigStride = 8
+                strides.update(
+                    {x for x in itertools.product(*([[1, bigStride]] * k_rank))}
+                )
+                bigDilation = 7
+                dilations.update(
+                    {x for x in itertools.product(*([[1, bigDilation]] * k_rank))}
+                )
+
+            if error_name is None:
+                # There are too many parameter combinations, so generate them sparsely,
+                sparsity_factor = 120
+                sparsity = TosaArgGen._calculate_sparsity(
+                    len(paddings) * len(strides) * len(dilations), sparsity_factor
+                )
+
+        # Run through all the argument options creating valid test cases
         more_tests = True
         n = 0
         for a in accum_dtypes:
             for s in sorted(list(strides)):
                 for p in sorted(list(paddings)):
                     for d in sorted(list(dilations)):
-                        if (
-                            more_tests
-                            and n % sparsity == 0
-                            # the padded shape must exceed the dilation * kernel to get a positive
-                            # sized output shape
-                            and (ifm_shape[1] - 1 + p[0] + p[1])
-                            > d[0] * (k_shape[0] - 1)
-                            and (ifm_shape[2] - 1 + p[2] + p[3])
-                            > d[1] * (k_shape[1] - 1)
-                            and (
-                                k_rank < 3
-                                or (
-                                    (ifm_shape[3] - 1 + p[4] + p[5])
-                                    > d[2] * (k_shape[2] - 1)
-                                )
-                            )
-                        ):
-                            remainders = []
-                            outputs = []
-                            for index in range(k_rank):
-                                pad_offset = index * 2
-                                partial = (
-                                    ifm_shape[index + 1]
-                                    - 1
-                                    + p[pad_offset]
-                                    + p[pad_offset + 1]
-                                    - (k_shape[index] - 1) * d[index]
-                                )
-                                remainders.append(partial % s[index])
-                                outputs.append((partial // s[index]) + 1)
+                        if more_tests and (n % sparsity == 0):
+                            (
+                                p,
+                                remainders,
+                                outputs,
+                                outputs_no_stride,
+                            ) = get_conv_output_info(p, s, d, fix_up_padding)
+                            # Following is like checking each dimension N:
+                            # (ifm_shape[N+1] - 1 + p[N*2] + p[N*2+1]) > d[N] * (k_shape[N] - 1)
+                            if min(outputs_no_stride) <= 0:
+                                # Not a valid operation
+                                n += 1  # Increment count of tests
+                                continue
 
                             if (
                                 # the parameters must produce integer exact output
@@ -2213,10 +2230,13 @@
                                     and max(outputs) >= max_dim_size
                                 ):
                                     # Test will consume too much memory - skip it
+                                    logger.debug(
+                                        "agConv: Convolution output too big - skipped"
+                                    )
                                     continue
 
                                 # Compliance - number of dot product calculations
-                                if depthwise:
+                                if op["op"] == Op.DEPTHWISE_CONV2D:
                                     # N*OH*OW*C*M
                                     dots = gtu.product(
                                         (ifm_shape[0], *outputs, *filter_shape[2:])
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 28b3d28..71d7fcc 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -3172,64 +3172,54 @@
             return False
 
     def createDynamicOpLists(self):
-
-        if "conv2d_TEMPLATE" not in self.TOSA_OP_LIST:
-            # Already created these lists (can occur when class is initialized more than once)
-            return
-
-        # Dynamically create op lists for convolutions with a list of kernel sizes
-        if not self.args.level8k:
-            KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]]
-            KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]]
-        else:
-            bigK = self.TOSA_8K_LEVEL_MAX_KERNEL
-            KERNELS_2D = [[1, bigK], [bigK, 2]]
-            KERNELS_3D = [[1, bigK, 1], [2, 2, bigK]]
-
-        for k in KERNELS_2D:
-            testName = "conv2d_{}x{}".format(k[0], k[1])
-            self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy()
-            self.TOSA_OP_LIST[testName]["filter"] = k
-            self.TOSA_OP_LIST[testName]["template"] = False
-            self.TOSA_OP_LIST[testName]["real_name"] = "conv2d"
-
-            testName = "depthwise_conv2d_{}x{}".format(k[0], k[1])
-            self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[
-                "depthwise_conv2d_TEMPLATE"
-            ].copy()
-            self.TOSA_OP_LIST[testName]["filter"] = k
-            self.TOSA_OP_LIST[testName]["template"] = False
-            self.TOSA_OP_LIST[testName]["real_name"] = "depthwise_conv2d"
-
-            testName = "transpose_conv2d_{}x{}".format(k[0], k[1])
-            self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[
-                "transpose_conv2d_TEMPLATE"
-            ].copy()
-            self.TOSA_OP_LIST[testName]["filter"] = k
-            self.TOSA_OP_LIST[testName]["template"] = False
-            self.TOSA_OP_LIST[testName]["real_name"] = "transpose_conv2d"
-
-        for k in KERNELS_3D:
-            testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2])
-            self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy()
-            self.TOSA_OP_LIST[testName]["filter"] = k
-            self.TOSA_OP_LIST[testName]["template"] = False
-            self.TOSA_OP_LIST[testName]["real_name"] = "conv3d"
-
-        # Delete any templates after having created any dynamic ops
-        # This is a two-pass operation because it's bad practice to delete
-        # keys from dictionaries while iterating
-        keyList = []
-        for k in self.TOSA_OP_LIST:
+        # Find all the ops marked as templates
+        templateKeys = []
+        for opName in self.TOSA_OP_LIST:
             try:
-                if self.TOSA_OP_LIST[k]["template"]:
-                    keyList.append(k)
-                    continue
+                if self.TOSA_OP_LIST[opName]["template"]:
+                    templateKeys.append(opName)
             except KeyError:
                 pass
 
-        for k in keyList:
-            del self.TOSA_OP_LIST[k]
+        bigK = self.TOSA_8K_LEVEL_MAX_KERNEL
+
+        # Add dynamic ops based on kernel sizes
+        for opName in templateKeys:
+            assert opName.endswith("_TEMPLATE"), "Found incorrect template"
+            realName = opName[: len(opName) - len("_TEMPLATE")]
+            template = self.TOSA_OP_LIST[opName]
+            k_rank = 3 if realName == "conv3d" else 2
+
+            # Choose kernels to build tests for from the template or args
+            if self.args.level8k:
+                if k_rank == 3:
+                    kernels = [[1, bigK, 1], [2, 2, bigK]]
+                else:
+                    kernels = [[1, bigK], [bigK, 2]]
+            else:
+                kernels = []
+                if len(self.args.conv_kernels) > 0:
+                    kernels = [k for k in self.args.conv_kernels if len(k) == k_rank]
+                    if len(kernels) == 0:
+                        logger.debug(
+                            f"{realName} op using defaults as no rank {k_rank} kernels found in {self.args.conv_kernels}"
+                        )
+                if len(kernels) == 0:
+                    # Fallback to use the defined template kernels
+                    kernels = self.TOSA_OP_LIST[opName]["filter"]
+
+            # Dynamically create ops for listed kernel sizes
+            for k in kernels:
+                kernelStr = "x".join([str(d) for d in k])
+                testName = f"{realName}_{kernelStr}"
+                kernelOp = template.copy()
+                kernelOp["filter"] = k
+                kernelOp["template"] = False
+                kernelOp["real_name"] = realName
+                self.TOSA_OP_LIST[testName] = kernelOp
+
+            # Delete the template after having created the dynamic ops
+            del self.TOSA_OP_LIST[opName]
 
     def initOpListDefaults(self):
         """Fill in default fields for ops if they aren't already specified.
@@ -3333,6 +3323,9 @@
 
     DEFAULT_RANK_RANGE = (1, gtu.MAX_TENSOR_RANK)
 
+    KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]]
+    KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]]
+
     TOSA_OP_LIST = {
         # Tensor operators
         "argmax": {
@@ -3426,6 +3419,8 @@
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
             },
+            "broadcastable_bias": True,
+            "filter": KERNELS_2D,
             "template": True,
         },
         # Templated operator.  Filled in by createDynamicOpLists
@@ -3460,13 +3455,13 @@
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
             },
+            "filter": KERNELS_3D,
             "template": True,
         },
         # Templated operator.  Filled in by createDynamicOpLists
         "depthwise_conv2d_TEMPLATE": {
             "op": Op.DEPTHWISE_CONV2D,
             "operands": (1, 2),
-            "filter": [1, 1],
             "rank": (4, 4),
             "build_fcn": (
                 build_depthwise_conv2d,
@@ -3495,6 +3490,7 @@
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
             },
+            "filter": KERNELS_2D,
             "template": True,
         },
         "fully_connected": {
@@ -3608,6 +3604,7 @@
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
             },
+            "filter": KERNELS_2D,
             "template": True,
         },
         # Activation functions
diff --git a/verif/generator/tosa_test_select.py b/verif/generator/tosa_test_select.py
index 904d90f..a894bf4 100644
--- a/verif/generator/tosa_test_select.py
+++ b/verif/generator/tosa_test_select.py
@@ -164,9 +164,6 @@
             op, selectionCriteria, "all", False
         )
 
-        if self.paramArgs and self.maximumPerPermute > 1:
-            logger.warning(f"Unsupported - selection params AND maximum for {opName}")
-
         self.tests = []
         self.testStrings = set()
         self.shapes = set()
diff --git a/verif/generator/tosa_verif_build_tests.py b/verif/generator/tosa_verif_build_tests.py
index 0839cec..f746009 100644
--- a/verif/generator/tosa_verif_build_tests.py
+++ b/verif/generator/tosa_verif_build_tests.py
@@ -168,6 +168,15 @@
     )
 
     ops_group.add_argument(
+        "--conv-kernel",
+        dest="conv_kernels",
+        action="append",
+        default=[],
+        type=lambda x: str_to_list(x),
+        help="Create convolution tests with a particular kernel shape, e.g., 1,4 or 1,3,1 (only 2D kernel sizes will be used for 2D ops, etc.)",
+    )
+
+    ops_group.add_argument(
         "--max-pooling-padding",
         dest="max_pooling_padding",
         default=1,