diff --git a/juno_samples/edge_detection/benches/edge_detection_bench.rs b/juno_samples/edge_detection/benches/edge_detection_bench.rs
index 49e67a29b4a23ae142318623e59207e4847b7495..01ae418a6de2923d9ce24069f699972f9bc81f09 100644
--- a/juno_samples/edge_detection/benches/edge_detection_bench.rs
+++ b/juno_samples/edge_detection/benches/edge_detection_bench.rs
@@ -87,7 +87,6 @@ fn edge_detection_bench(c: &mut Criterion) {
                     r.run(
                         height as u64,
                         width as u64,
-                        gs as u64,
                         input_h.to(),
                         gaussian_filter_h,
                         structure_h,
diff --git a/juno_samples/edge_detection/src/edge_detection.jn b/juno_samples/edge_detection/src/edge_detection.jn
index da1977b67ec809ae0b81cbe0873e0a8552912897..be98781210791bddb7aa11ac353457e791c1d4b0 100644
--- a/juno_samples/edge_detection/src/edge_detection.jn
+++ b/juno_samples/edge_detection/src/edge_detection.jn
@@ -1,6 +1,10 @@
-fn gaussian_smoothing<n, m, gs : usize>(
+const gs : usize = 7;
+const sz : usize = 3;
+const sb : usize = 3;
+
+fn gaussian_smoothing<n, m : usize>(
   input: f32[n, m],
-  filter: f32[gs, gs],
+  filter: f32[7, 7],
 ) -> f32[n, m] {
   @res let result : f32[n, m];
 
@@ -34,7 +38,6 @@ fn laplacian_estimate<n, m : usize>(
   input: f32[n, m],
   structure: f32[3, 3],
 ) -> f32[n, m] {
-  const sz = 3;
   const r = sz / 2;
 
   @res let result : f32[n, m];
@@ -79,7 +82,6 @@ fn zero_crossings<n, m : usize>(
   input: f32[n, m],
   structure: f32[3, 3],
 ) -> f32[n, m] {
-  const sz = 3;
   const r = sz / 2;
 
   @res let result : f32[n, m];
@@ -127,7 +129,6 @@ fn gradient<n, m : usize>(
   sx: f32[3, 3],
   sy: f32[3, 3],
 ) -> f32[n, m] {
-  const sb = 3;
   const sbr = sb / 2;
 
   @res let result : f32[n, m];
@@ -191,7 +192,7 @@ fn reject_zero_crossings<n, m: usize>(
 }
 
 #[entry]
-fn edge_detection<n, m, gs: usize>(
+fn edge_detection<n, m : usize>(
   input: f32[n, m],
   gaussian_filter: f32[gs, gs],
   structure: f32[3, 3],
@@ -199,7 +200,7 @@ fn edge_detection<n, m, gs: usize>(
   sy: f32[3, 3],
   theta: f32,
 ) -> f32[n, m] {
-  let smoothed = gaussian_smoothing::<n, m, gs>(input, gaussian_filter);
+  let smoothed = gaussian_smoothing::<n, m>(input, gaussian_filter);
   @le let laplacian = laplacian_estimate::<n, m>(smoothed, structure);
   @zc let zcs = zero_crossings::<n, m>(laplacian, structure);
   let gradient = gradient::<n, m>(smoothed, sx, sy);
diff --git a/juno_samples/edge_detection/src/lib.rs b/juno_samples/edge_detection/src/lib.rs
index 3eaf38dc34f1b4bb12bbc95c1739a58cdcc41825..c1d04b0f9a7b4950f9b8bf6ff6438b46291cb2f3 100644
--- a/juno_samples/edge_detection/src/lib.rs
+++ b/juno_samples/edge_detection/src/lib.rs
@@ -194,7 +194,6 @@ pub fn edge_detection_harness(args: EdgeDetectionInputs) {
                 r.run(
                     height as u64,
                     width as u64,
-                    gs as u64,
                     input_h.to(),
                     gaussian_filter_h.to(),
                     structure_h.to(),