sw: add fused MLP layer to transformer

sw/apps/transformer/src/transformer.h

@@ -83,6 +83,61 @@ dump_float(ifmap, 6);
 dump_float(weights, 10); // = 0xa
 dump_float(value, 12); // = 0xc
 
+/**
+ * Implementation of the GELU layer
+ */
+#define M_PI 3.14159265358979323846
+static inline float transformer_gelu_fp32(float x) {
+    float y = 0.5 * x * (1.0 + tanh(sqrt(2.0 / M_PI) * (x + 0.044715 * x * x * x)));
+    return y;
+}
+
+/**
+ * Implementation of the LayerNorm layer fused with a linear layer and a GELU activation.
+ * Input is a 2D matrix of size (S x E) where S is the sequence length and E is the number of embeddings.
+ * The weights matrix is of size (E x F) where F is the number of hidden nodes.
+ */
+static inline void fused_mlp_baseline(float *input, float *output, int32_t ldI, int32_t ldO, float *weights, int32_t ldW,
+                                                  int32_t seq_len, int32_t embeddings, int32_t ff_len, int32_t eps) {
+    float mean = 0.0;  // max value of the current core
+    float var = 0.0;   // sum of the exp values of the current core
+    float acc = 0.0;   // accumulator for the linear layer
+
+    uint32_t compute_id = snrt_global_core_idx();
+    uint32_t num_cores = snrt_cluster_compute_core_num();
+
+    // compute the mean and variance along the innermost dimension
+    for (int s = 0; s < seq_len; s++) {
+        mean = 0.0;
+        var = 0.0;
+        for (int e = 0; e < embeddings; e++) {
+            mean += input[s * ldI + e];
+        }
+
+        mean /= embeddings;
+
+        for (int e = 0; e < embeddings; e++) {
+            var += (input[s * ldI + e] - mean) * (input[s * ldI + e] - mean);
+        }
+        var /= embeddings;
+        // we have to compute the normalize row only once
+        // and then multiply with the columns of the weight matrix
+        for (int f = 0; f < ff_len; f++) {
+            acc = 0.0;
+            for (int e = 0; e < embeddings; e++) {
+                // we only have to compute the normalization once
+                if (f == 0) {
+                    output[s * ldO + f] = (input[s * ldI + e] - mean) / sqrtf(var + eps);
+                }
+                acc += output[s * ldO + e] * weights[e * ldW + f];
+            }
+            output[s * ldO + f] = transformer_gelu_fp32(acc);
+        }
+    }
+
+
+}
+
 /**
  * @brief  Transformer layer
  *