1 files changed, 36 insertions, 1 deletions
diff --git a/tests/validation/Helpers.cpp b/tests/validation/Helpers.cpp
index 2f273e7042..cb4d87601c 100644
--- a/tests/validation/Helpers.cpp
+++ b/tests/validation/Helpers.cpp
@@ -352,8 +352,43 @@ void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &d
     }
 }
 
+QuantizationHint suggest_conv_dst_q_info_and_bias(const QuantizationInfo &in_q_info,
+                                                  const QuantizationInfo &weight_q_info,
+                                                  int32_t height,
+                                                  int32_t width,
+                                                  int32_t channels,
+                                                  DataType data_type,
+                                                  float bias_fraction)
+{
+    /**  Quantization Setup of convolution
+     *
+     *  Just like any other multiply-accummulate, convolution (2D) operation
+     *  multiplies and accumulates the input and weight tensors. This operation
+     *  takes place in three dimensions: height, width and channels. All of them
+     *  belong to the weight tensor.
+     *
+     *  The formula for simple convolution can be written as:
+     *      C = sum_h sum_w sum_c(I[h_offset + h, w_offset + w, c] * W[h, w, c])
+     *
+     *  Here, h_offset and w_offset are the starting positions in the image. Effects
+     *  of paddings are ignored. This accumulation reduces to something like
+     *
+     *  C = sum_m(I_index * W_hwc)
+     *      where m is height x width x channels.
+     *
+     *  Non-unit strides and/or dilations do not change the probabilistic nature of
+     *  this sum because we always iterate as the size of the weight tensor.
+     *
+     *  Paddings may affect this summation, but it's a boundary condition and so is
+     *  neglected for brevity.
+     */
+
+    return suggest_mac_dst_q_info_and_bias(in_q_info, weight_q_info, height * width * channels, data_type, bias_fraction);
+}
+
 QuantizationHint suggest_matmul_dst_q_info_and_bias(const QuantizationInfo &lhs_q_info,
-                                                    const QuantizationInfo &rhs_q_info, int32_t m, int32_t n, int32_t k, DataType data_type,
+                                                    const QuantizationInfo &rhs_q_info,
+                                                    int32_t m, int32_t n, int32_t k, DataType data_type,
                                                     float bias_fraction)
 {
     ARM_COMPUTE_UNUSED(m, n);