diff --git a/ethosu/vela/tosa_supported_operators.py b/ethosu/vela/tosa_supported_operators.py
index 98df27e..f5eddcc 100644
--- a/ethosu/vela/tosa_supported_operators.py
+++ b/ethosu/vela/tosa_supported_operators.py
@@ -40,15 +40,15 @@
     mac_main_ops = convolution_like_ops | pooling_ops | fc_vector_products
     memory_only_ops = set((Op.Reshape, Op.Transpose, Op.Concat, Op.SplitSliceRead,))
     binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.RescaleMul, Op.Sub,))
+    elem_wise_ops = binary_elem_wise_add_mul_sub
     type_conversion_ops = set((Op.Rescale,))
     relu_ops = set((Op.Clamp, Op.ReluN,))
     activation_ops = relu_ops | set((Op.Table,))
     pad_ops = set((Op.Pad,))
 
     npu_post_ops = activation_ops
-    supported_operators = (
-        mac_main_ops | type_conversion_ops | npu_post_ops | memory_only_ops | binary_elem_wise_add_mul_sub | pad_ops
-    )
+
+    supported_operators = mac_main_ops | type_conversion_ops | npu_post_ops | memory_only_ops | elem_wise_ops | pad_ops
 
     # Supported data types
     # TODO will differ compared to TensorFlow Lite, currently set to the same
@@ -132,35 +132,37 @@
         return valid, ", ".join(extra)
 
     # TODO This is for a HW limitation, that is to be resolved in SW later on
-    @staticmethod
-    def constraint_rank(op):
-        "Tensor rank must be <= 4"
+    @classmethod
+    def constraint_rank(self, op):
+        "Tensor rank must be <= 4, if not elementwise"
         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:
-            rank = len(tens.shape)
-            if not rank <= 4:
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has rank: {rank}")
+        if op.type not in self.binary_elem_wise_add_mul_sub:
+            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:
+                rank = len(tens.shape)
+                if not rank <= 4:
+                    valid = False
+                    extra.append(f"Tensor '{tens.name}' has rank: {rank}")
         return valid, ", ".join(extra)
 
     # TODO This is for a HW limitation, that is to be resolved in SW later on
-    @staticmethod
-    def constraint_batch(op):
-        "If Tensor rank is 4 batch of ifms/ofm must be 1"
+    @classmethod
+    def constraint_batch(self, op):
+        "If Tensor rank is 4 batch of ifms/ofm must be 1, if not elementwise"
         valid = True
         extra = []
-        tensors = [tens for tens in op.get_ifm_ifm2_ofm() if tens]
-        if not tensors:
-            tensors = [tens for tens in op.inputs if tens]
-        for tens in tensors:
-            rank = len(tens.shape)
-            if rank == 4 and tens.shape[0] != 1:
-                valid = False
-                extra.append(f"Tensor '{tens.name}' has rank: 4 and N: {tens.shape[0]}")
+        if op.type not in self.binary_elem_wise_add_mul_sub:
+            tensors = [tens for tens in op.get_ifm_ifm2_ofm() if tens]
+            if not tensors:
+                tensors = [tens for tens in op.inputs if tens]
+            for tens in tensors:
+                rank = len(tens.shape)
+                if rank == 4 and tens.shape[0] != 1:
+                    valid = False
+                    extra.append(f"Tensor '{tens.name}' has rank: 4 and N: {tens.shape[0]}")
         return valid, ", ".join(extra)
 
     @staticmethod