diff --git a/reference_model/src/ops/ewise_binary.cc b/reference_model/src/ops/ewise_binary.cc
index 6808604..415cd1c 100644
--- a/reference_model/src/ops/ewise_binary.cc
+++ b/reference_model/src/ops/ewise_binary.cc
@@ -490,8 +490,10 @@
                                 uint64_t id_)
     : GraphNode(sgt_, Op_TABLE, id_)
 {
-    setRequiredOperands(2, 1);
+    setRequiredOperands(1, 1);
     setRequiredRank(0, 6);
+
+    INIT_ATTRIBUTE(Table);
 }
 
 template <int Rank, DType InDtype>
@@ -509,36 +511,18 @@
         return 1;
     }
 
-    if (inputs[1]->getRank() != 1)
+    ERROR_IF(inputs[0]->getDtype() != InDtype, "OpTable: Unexpected input type");
+    ERROR_IF(attribute->table().size() != TableNumEntries, "OpTable: table attribute size must be %u", TableNumEntries);
+
+    for (uint32_t i = 0; i < TableNumEntries; i++)
     {
-        printNodeValidationError("OpTable: Table must be rank 1 tensor");
-        return 1;
+        table[i] = (TableEigenType)attribute->table()[i];
     }
 
-    if (inputs[0]->getDtype() == DType_INT8)
-    {
-        if (inputs[1]->getElementCount() != 256 || inputs[1]->getDtype() != DType_INT8)
-        {
-            printNodeValidationError("OpTable: Table must be INT8[256] if input is INT8");
-            return 1;
-        }
-        ERROR_IF(outputs[0]->getDtype() != DType_INT8, "OpTable: output tensor must be INT8");
-    }
-    else if (inputs[0]->getDtype() == DType_INT16)
-    {
-        if (inputs[1]->getElementCount() != 513 || inputs[1]->getDtype() != DType_INT16)
-        {
-            printNodeValidationError("OpTable: Table must be INT16[513] if input is INT16");
-            return 1;
-        }
-        ERROR_IF(outputs[0]->getDtype() != DType_INT32, "OpTable: output tensor must be INT32");
-    }
+    in  = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+    out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
 
-    in    = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
-    table = dynamic_cast<TosaReference::TensorTemplate<TTable>*>(inputs[1]);
-    out   = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
-
-    ASSERT_MEM(in && table && out);
+    ASSERT_MEM(in && out);
 
     return 0;
 }
@@ -552,7 +536,7 @@
             this->out->getTensor() = this->in->getTensor().unaryExpr([this](InEigenType in) -> OutEigenType {
                 int32_t input_truncated = std::min<int32_t>(std::max<int32_t>(in, QInMin), QInMax);
                 int32_t index           = input_truncated - QInMin;
-                int32_t value           = this->table->getTensor()(index);
+                int32_t value           = table[index];
 
                 return value;
             });
@@ -568,8 +552,8 @@
                 int32_t frac  = (input_truncated)&0x7F;    // 7-bit fraction
 
                 // 3. interpolate, generate 16.7 (23-bit) output
-                int32_t base  = this->table->getTensor()(index);
-                int32_t next  = this->table->getTensor()(index + 1);
+                int32_t base  = table[index];
+                int32_t next  = table[index + 1];
                 int32_t value = (base << 7) + (next - base) * frac;
 
                 return value;