diff --git a/ethosu/vela/tosa_supported_operators.py b/ethosu/vela/tosa_supported_operators.py
index c619f2f..d368616 100644
--- a/ethosu/vela/tosa_supported_operators.py
+++ b/ethosu/vela/tosa_supported_operators.py
@@ -54,11 +54,13 @@
     # Supported data types
     # TODO will differ compared to TensorFlow Lite, currently set to the same
     supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))  # TODO add bool
+    tens_dim_range = (1, 65535)  # TODO HW limitation, that is to be resolved in SW
 
     def __init__(self):
         # Setup the generic constraints. Note: the order matters
         self.generic_constraints = []
         self.generic_constraints.append(TosaSupportedOperators.constraint_tens_dtype)
+        self.generic_constraints.append(TosaSupportedOperators.constraint_tens_dimension)
 
         # Setup specific constraints. Note: the order matters
         self.specific_constraints = defaultdict(list)
@@ -69,6 +71,10 @@
         for op_type in TosaSupportedOperators.depthwise_convolution_ops:
             self.specific_constraints[op_type].append(TosaSupportedOperators.constraint_depth_multiplier)
 
+        # Avgpool specific checks
+        for op_type in TosaSupportedOperators.avg_pooling_ops:
+            self.specific_constraints[op_type].append(TosaSupportedOperators.constraint_padding)
+
     def is_operator_supported(self, op):
         ext_type = optype_to_tosa_op_type(op.type)
         if op.type not in TosaSupportedOperators.supported_operators:
@@ -103,13 +109,41 @@
                 extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}")
         return valid, ", ".join(extra)
 
+    # TODO Duplicates check present for TFLite. But it is only temporarily added
+    # This is for a HW limitation, that is to be resolved in SW later on
+    @classmethod
+    @docstring_format_args(tens_dim_range)
+    def constraint_tens_dimension(cls, op):
+        "Tensor dimensions must be in the range [{}, {}]"
+        tens_min, tens_max = cls.tens_dim_range
+        valid = True
+        extra = []
+        tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
+        if not tensors:
+            tensors = [tens for tens in op.inputs if tens]
+        for tens in tensors:
+            if not all(tens_min <= dim <= tens_max for dim in tens.shape):
+                valid = False
+                extra.append(f"Tensor '{tens.name}' has shape: {tens.shape}")
+        return valid, ", ".join(extra)
+
     @staticmethod
     def constraint_ifm_producer(cls, op):
         "Input must be constant data"
         valid = op.ifm.ops and op.ifm.ops[0].type == Op.Const
         return valid, "Op has ifm with non-constant data"
 
-    # TODO duplicates TFLite_supported operators, but support for depth multiplier should be added at a later stage
+    @staticmethod
+    def constraint_padding(op):
+        # TODO Only support for when global scaling can be used.
+        # That is when there is padding no padding
+        "Avgpool only supported for no padding"
+        top, left, _, _ = op.attrs["explicit_padding"]
+        valid = top == 0 and left == 0
+
+        return valid, "Avgpool with pad_top {top} and pad_left {left}"
+
+    # TODO duplicates tflite_supported operators, but support for depth multiplier should be added at a later stage
     @staticmethod
     def constraint_depth_multiplier(op):
         "For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier"