diff --git a/src/cnn/convolution.c b/src/cnn/convolution.c
index 0589462..c280b71 100644
--- a/src/cnn/convolution.c
+++ b/src/cnn/convolution.c
@@ -42,27 +42,27 @@ void make_convolution_cpu(Kernel_cnn* kernel, float*** input, float*** output, i
 
 #ifdef __CUDACC__
 
-__global__ void make_convolution_kernel(Kernel_cnn* kernel, float*** input, float*** output, int output_width, int stride, int padding) {
+__global__ void make_convolution_kernel(float**** weights, float*** bias, int k_size, int rows, int columns, float*** input, float*** output, int output_width, int stride, int padding) {
     // Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
     int idx = threadIdx.x + blockDim.x*blockIdx.x; // < kernel->columns
     int idy = threadIdx.y + blockDim.y*blockIdx.y; // < min(output_width, k_size)
     int idz = threadIdx.z + blockDim.z*blockIdx.z; // < min(output_width, k_size)
-    int max_move = kernel->k_size - padding;
-    int input_width = output_width*stride - 2*padding + kernel->k_size - stride;
+    int max_move = k_size - padding;
+    int input_width = output_width*stride - 2*padding + k_size - stride;
 
-    if (idx >= kernel->columns || idy >= output_width || idz >= output_width) {
+    if (idx >= columns || idy >= output_width || idz >= output_width) {
         return;
     }
 
-    float f = kernel->bias[idx][idy][idz];
+    float f = bias[idx][idy][idz];
 
-    for (int a=0; a < kernel->rows; a++) {
+    for (int a=0; a < rows; a++) {
         for (int b=-padding; b < max_move; b++) {
             for (int c=-padding; c < max_move; c++) {
                 int idy_2 = idy*stride+b;
                 int idz_2 = idz*stride+c;
                 if (not_outside(idy_2, idz_2, 0, input_width)) {
-                    f += kernel->weights[a][idx][b][c]*input[a][idy_2][idz_2];
+                    f += weights[a][idx][b][c]*input[a][idy_2][idz_2];
                 }
             }
         }
@@ -76,7 +76,9 @@ void make_convolution_device(Kernel_cnn* kernel, float*** input, float*** output
     dim3 gridSize(i_div_up(kernel->columns, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_z));
     dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
 
-    make_convolution_kernel<<<gridSize, blockSize>>>(kernel, input, output, output_width, stride, padding);
+    // We can't pass `kernel` directly to the CUDA kernel function
+    // as it will create a 'misaligned adress' error
+    make_convolution_kernel<<<gridSize, blockSize>>>(kernel->weights, kernel->bias, kernel->k_size, kernel->rows, kernel->columns, input, output, output_width, stride, padding);
     gpuErrchk( cudaPeekAtLastError() );
     gpuErrchk( cudaDeviceSynchronize() );
 }
diff --git a/src/cnn/convolution.cu b/src/cnn/convolution.cu
index 0589462..c280b71 100644
--- a/src/cnn/convolution.cu
+++ b/src/cnn/convolution.cu
@@ -42,27 +42,27 @@ void make_convolution_cpu(Kernel_cnn* kernel, float*** input, float*** output, i
 
 #ifdef __CUDACC__
 
-__global__ void make_convolution_kernel(Kernel_cnn* kernel, float*** input, float*** output, int output_width, int stride, int padding) {
+__global__ void make_convolution_kernel(float**** weights, float*** bias, int k_size, int rows, int columns, float*** input, float*** output, int output_width, int stride, int padding) {
     // Équivalents respectifs de i, j et k dans la boucle effectuée par le cpu
     int idx = threadIdx.x + blockDim.x*blockIdx.x; // < kernel->columns
     int idy = threadIdx.y + blockDim.y*blockIdx.y; // < min(output_width, k_size)
     int idz = threadIdx.z + blockDim.z*blockIdx.z; // < min(output_width, k_size)
-    int max_move = kernel->k_size - padding;
-    int input_width = output_width*stride - 2*padding + kernel->k_size - stride;
+    int max_move = k_size - padding;
+    int input_width = output_width*stride - 2*padding + k_size - stride;
 
-    if (idx >= kernel->columns || idy >= output_width || idz >= output_width) {
+    if (idx >= columns || idy >= output_width || idz >= output_width) {
         return;
     }
 
-    float f = kernel->bias[idx][idy][idz];
+    float f = bias[idx][idy][idz];
 
-    for (int a=0; a < kernel->rows; a++) {
+    for (int a=0; a < rows; a++) {
         for (int b=-padding; b < max_move; b++) {
             for (int c=-padding; c < max_move; c++) {
                 int idy_2 = idy*stride+b;
                 int idz_2 = idz*stride+c;
                 if (not_outside(idy_2, idz_2, 0, input_width)) {
-                    f += kernel->weights[a][idx][b][c]*input[a][idy_2][idz_2];
+                    f += weights[a][idx][b][c]*input[a][idy_2][idz_2];
                 }
             }
         }
@@ -76,7 +76,9 @@ void make_convolution_device(Kernel_cnn* kernel, float*** input, float*** output
     dim3 gridSize(i_div_up(kernel->columns, BLOCKSIZE_x), i_div_up(output_width, BLOCKSIZE_y), i_div_up(output_width, BLOCKSIZE_z));
     dim3 blockSize(BLOCKSIZE_x, BLOCKSIZE_y, BLOCKSIZE_z);
 
-    make_convolution_kernel<<<gridSize, blockSize>>>(kernel, input, output, output_width, stride, padding);
+    // We can't pass `kernel` directly to the CUDA kernel function
+    // as it will create a 'misaligned adress' error
+    make_convolution_kernel<<<gridSize, blockSize>>>(kernel->weights, kernel->bias, kernel->k_size, kernel->rows, kernel->columns, input, output, output_width, stride, padding);
     gpuErrchk( cudaPeekAtLastError() );
     gpuErrchk( cudaDeviceSynchronize() );
 }
diff --git a/src/cnn/include/convolution.h b/src/cnn/include/convolution.h
index 6919f5c..fc2c08a 100644
--- a/src/cnn/include/convolution.h
+++ b/src/cnn/include/convolution.h
@@ -10,7 +10,7 @@ void make_convolution_cpu(Kernel_cnn* kernel, float*** input, float*** output, i
 /*
 * Kernel de la convolution sur carte graphique
 */
-__global__ void make_convolution_kernel(int k_size, int columns, int rows, float* bias, size_t pitch_bias, float**** weights, size_t pitch_weights, float*** input, size_t pitch_input, float*** output, size_t pitch_output, int output_width, int stride, int padding);
+__global__ void make_convolution_kernel(float**** weights, float*** bias, int k_size, int rows, int columns, float*** input, float*** output, int output_width, int stride, int padding);
 
 /*
 * Effectue la convolution naïvement sur la carte graphique