diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 7702753..c867070 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -896,6 +896,7 @@
             input_shape=ifm.shape,
             weight_shape=filter.shape,
             output_shape=result_tensor.shape,
+            accum_dtype=accum_dtype,
         ):
             return None
 
@@ -903,7 +904,9 @@
         local_bound = False
 
         attr = ts.TosaSerializerAttribute()
-        attr.ConvAttribute(padding, strides, dilations, qinfo[0], qinfo[1], local_bound)
+        attr.ConvAttribute(
+            padding, strides, dilations, qinfo[0], qinfo[1], local_bound, accum_dtype
+        )
 
         self.ser.addOperator(op["op"], input_list, output_list, attr)
 
@@ -981,6 +984,7 @@
             input_shape=ifm.shape,
             weight_shape=filter.shape,
             output_shape=result_tensor.shape,
+            accum_dtype=accum_dtype,
         ):
             return None
 
@@ -988,7 +992,9 @@
         local_bound = False
 
         attr = ts.TosaSerializerAttribute()
-        attr.ConvAttribute(padding, strides, dilations, qinfo[0], qinfo[1], local_bound)
+        attr.ConvAttribute(
+            padding, strides, dilations, qinfo[0], qinfo[1], local_bound, accum_dtype
+        )
 
         self.ser.addOperator(op["op"], input_list, output_list, attr)
 
@@ -1057,6 +1063,7 @@
             input_shape=ifm.shape,
             weight_shape=filter.shape,
             output_shape=result_tensor.shape,
+            accum_dtype=accum_dtype,
         ):
             return None
 
@@ -1065,7 +1072,7 @@
 
         attr = ts.TosaSerializerAttribute()
         attr.TransposeConvAttribute(
-            out_pad, strides, output_shape, qinfo[0], qinfo[1], local_bound
+            out_pad, strides, output_shape, qinfo[0], qinfo[1], local_bound, accum_dtype
         )
 
         self.ser.addOperator(op["op"], input_list, output_list, attr)
@@ -1143,6 +1150,7 @@
             input_shape=ifm.shape,
             weight_shape=filter.shape,
             output_shape=result_tensor.shape,
+            accum_dtype=accum_dtype,
         ):
             return None
 
@@ -1150,7 +1158,9 @@
         local_bound = False
 
         attr = ts.TosaSerializerAttribute()
-        attr.ConvAttribute(padding, strides, dilations, qinfo[0], qinfo[1], local_bound)
+        attr.ConvAttribute(
+            padding, strides, dilations, qinfo[0], qinfo[1], local_bound, accum_dtype
+        )
 
         self.ser.addOperator(op["op"], input_list, output_list, attr)
 
@@ -3385,6 +3395,7 @@
                 TosaErrorValidator.evWrongRank,
                 TosaErrorValidator.evConvOutputShapeMismatch,
                 TosaErrorValidator.evConvOutputShapeNonInteger,
+                TosaErrorValidator.evWrongAccumulatorType,
             ),
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
@@ -3418,6 +3429,7 @@
                 TosaErrorValidator.evWrongRank,
                 TosaErrorValidator.evConvOutputShapeMismatch,
                 TosaErrorValidator.evConvOutputShapeNonInteger,
+                TosaErrorValidator.evWrongAccumulatorType,
             ),
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
@@ -3452,6 +3464,7 @@
                 TosaErrorValidator.evWrongRank,
                 TosaErrorValidator.evConvOutputShapeMismatch,
                 TosaErrorValidator.evConvOutputShapeNonInteger,
+                TosaErrorValidator.evWrongAccumulatorType,
             ),
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
@@ -3564,6 +3577,7 @@
                 TosaErrorValidator.evStrideSmallerOne,
                 TosaErrorValidator.evWrongRank,
                 TosaErrorValidator.evConvOutputShapeMismatch,
+                TosaErrorValidator.evWrongAccumulatorType,
             ),
             "data_gen": {
                 "fp": (gtu.DataGenType.DOT_PRODUCT,),
@@ -5290,6 +5304,18 @@
         return ser.addOutput(shape, outputDType)
 
     @staticmethod
+    def _get_conv_output_type(input_dtype):
+        if input_dtype in (DType.FP16, DType.BF16, DType.FP32):
+            return input_dtype
+        elif input_dtype in (DType.FP8E4M3, DType.FP8E5M2):
+            return DType.FP16
+        elif input_dtype in (DType.INT8, DType.INT4):
+            return DType.INT32
+        elif input_dtype in (DType.INT16,):
+            return DType.INT48
+        assert True, f"Unsupported convolution data type {input_dtype}"
+
+    @staticmethod
     def conv2dOp(
         ser, rng, ifm, filter, accum_dtype, strides, padding, dilations, error_name=None
     ):
@@ -5329,7 +5355,7 @@
             # Pick some potentially correct output dtype if input type is incorrect
             out_dtype = DType.INT32
         else:
-            out_dtype = accum_dtype
+            out_dtype = OutputShaper._get_conv_output_type(ifm.dtype)
 
         if error_name == ErrorIf.WrongOutputType:
             if ifm.dtype == DType.FP16:
@@ -5393,7 +5419,7 @@
             # Pick some potentially correct output dtype if input type is incorrect
             out_dtype = DType.INT32
         else:
-            out_dtype = accum_dtype
+            out_dtype = OutputShaper._get_conv_output_type(ifm.dtype)
 
         if error_name == ErrorIf.WrongOutputType:
             if ifm.dtype == DType.FP16:
@@ -5444,7 +5470,7 @@
             # Pick some potentially correct output dtype if input type is incorrect
             out_dtype = DType.INT32
         else:
-            out_dtype = accum_dtype
+            out_dtype = OutputShaper._get_conv_output_type(ifm.dtype)
 
         if error_name == ErrorIf.WrongOutputType:
             if ifm.dtype == DType.FP16:
@@ -5958,7 +5984,7 @@
             # Pick some potentially correct output dtype if input type is incorrect
             out_dtype = DType.INT32
         else:
-            out_dtype = accum_dtype
+            out_dtype = OutputShaper._get_conv_output_type(ifm.dtype)
 
         if error_name == ErrorIf.WrongOutputType:
             if ifm.dtype == DType.FP16: