diff --git a/wonnx/Cargo.toml b/wonnx/Cargo.toml
index 4b941f22..fd250d32 100644
--- a/wonnx/Cargo.toml
+++ b/wonnx/Cargo.toml
@@ -45,3 +45,4 @@ ndarray = "0.15.4"
 approx = "0.5.1"
 pollster = "0.3.0"
 env_logger = "0.10.0"
+ndarray-rand = "0.14.0"
diff --git a/wonnx/src/compiler.rs b/wonnx/src/compiler.rs
index 41001e7c..009f4a42 100644
--- a/wonnx/src/compiler.rs
+++ b/wonnx/src/compiler.rs
@@ -42,11 +42,6 @@ fn get_templates() -> &'static Tera {
             include_str!("../templates/endomorphism/softmax.wgsl"),
         )
         .unwrap();
-        tera.add_raw_template(
-            "endomorphism/erf.wgsl",
-            include_str!("../templates/endomorphism/erf.wgsl"),
-        )
-        .unwrap();
         tera.add_raw_template(
             "endomorphism/map.wgsl",
             include_str!("../templates/endomorphism/map.wgsl"),
@@ -306,20 +301,6 @@ pub fn compile(
             }
         }
 
-        "Erf" => {
-            let (x_threads, workgroup_size_x) = workgroup_size(
-                ceil(output_lengths[0], 4),
-                MAX_COMPUTE_WORKGROUPS_PER_DIMENSION,
-                MAX_WORKGROUP_SIZE_X,
-            )?;
-            context.insert("workgroup_size_x", &workgroup_size_x);
-            NodeTemplate {
-                scalar_type: agreed_type(input_shapes, output_shapes)?,
-                template: "endomorphism/erf.wgsl",
-                threads: (x_threads, 1, 1),
-            }
-        }
-
         op @ ("ReduceMean" | "ReduceSum" | "ReduceMax" | "ReduceMin" | "ReduceProd"
         | "ReduceL1" | "ReduceL2" | "ReduceLogSum" | "ReduceLogSumExp"
         | "ReduceSumSquare") => {
@@ -762,7 +743,7 @@ pub fn compile(
             }
         }
         op @ ("Relu" | "Sigmoid" | "Softsign" | "Softplus" | "Clip" | "Celu" | "Elu"
-        | "LeakyRelu" | "HardSigmoid") => {
+        | "LeakyRelu" | "HardSigmoid" | "Erf") => {
             let alpha = match op {
                 "LeakyRelu" => node.get_attribute_value("alpha", Some(0.01))?,
                 "HardSigmoid" => node.get_attribute_value("alpha", Some(0.2))?,
diff --git a/wonnx/templates/endomorphism/activation.wgsl b/wonnx/templates/endomorphism/activation.wgsl
index 0b9cf649..6b76392d 100644
--- a/wonnx/templates/endomorphism/activation.wgsl
+++ b/wonnx/templates/endomorphism/activation.wgsl
@@ -6,6 +6,8 @@ var<storage, read> input_0: ArrayVector;
 @group(0) @binding(1)
 var<storage, read_write> output_0: ArrayVector;
 
+const pi: f32 = 3.1415;
+
 @compute @workgroup_size({{ workgroup_size_x }})
 fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
 	let gidx = global_id.x;
diff --git a/wonnx/templates/endomorphism/erf.wgsl b/wonnx/templates/endomorphism/erf.wgsl
deleted file mode 100644
index 4d321e71..00000000
--- a/wonnx/templates/endomorphism/erf.wgsl
+++ /dev/null
@@ -1,16 +0,0 @@
-{%- include "structs.wgsl" -%}
-@group(0) @binding(0)
-var<storage, read> input_0: ArrayVector;
-
-const pi: f32 = 3.1415;
-
-@group(0) @binding(1)
-var<storage, read_write> output_0: ArrayVector;
-
-@compute @workgroup_size({{ workgroup_size_x }})
-fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
-	let gidx = global_id.x;
-    var intermediate = 2.0/sqrt(pi)*(input_0.data[gidx]+ pow(input_0.data[gidx],vec4(3.0,3.0,3.0,3.0))*0.08943 );
-    intermediate = clamp(intermediate,vec4(-10.0,-10.0,-10.0,-10.0),vec4(10.0,10.0,10.0,10.0));
-	output_0.data[gidx] = tanh(intermediate);
-}
\ No newline at end of file
diff --git a/wonnx/templates/snippets/activation_vec.wgsl b/wonnx/templates/snippets/activation_vec.wgsl
index c1e183dc..9327f236 100644
--- a/wonnx/templates/snippets/activation_vec.wgsl
+++ b/wonnx/templates/snippets/activation_vec.wgsl
@@ -46,6 +46,11 @@
 	{{ activation_output }} = max({{ activation_input }}, Vec4(Scalar(), Scalar(), Scalar(), Scalar()))
 	                         + min({{ scalar_type }}({{ alpha }}) * {{ activation_input }}, Vec4(Scalar(), Scalar(), Scalar(), Scalar()));
 
+{%- elif activation_type == "Erf" -%}
+    var intermediate = 2.0/sqrt(pi)*({{ activation_input }}+ pow({{activation_input}},vec4(3.0,3.0,3.0,3.0))*0.08943 );
+    intermediate = clamp(intermediate,vec4(-10.0,-10.0,-10.0,-10.0),vec4(10.0,10.0,10.0,10.0));
+	{{ activation_output }} = tanh(intermediate);
+
 {%- elif activation_type == "HardSigmoid" -%}
 	{{ activation_output }} = max(
 		Vec4(Scalar(), Scalar(), Scalar(), Scalar()),
diff --git a/wonnx/tests/arithmetic.rs b/wonnx/tests/arithmetic.rs
index 057d8852..655a380f 100644
--- a/wonnx/tests/arithmetic.rs
+++ b/wonnx/tests/arithmetic.rs
@@ -1,5 +1,4 @@
 use approx::assert_abs_diff_eq;
-use log::error;
 use std::{collections::HashMap, convert::TryInto};
 use wonnx::{
     onnx::TensorProto_DataType,
@@ -9,11 +8,12 @@ use wonnx::{
     },
 };
 
+use ndarray_rand::rand_distr::Uniform;
+use ndarray_rand::RandomExt;
+
 mod common;
 #[test]
 fn test_erf() {
-    use ndarray_rand::rand_distr::Uniform;
-    use ndarray_rand::RandomExt;
     let n: usize = 16;
     let mut input_data = HashMap::new();
     let data = ndarray::Array1::<f32>::random(n, Uniform::new(-100f32, 100f32));