fp16 kernel

tests/regression/sgemm_gemmini_dma/generate_operands.py

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+import numpy as np
+
+# Function to generate random fp16 values
+def generate_fp16_matrix(size):
+    return np.random.rand(size, size).astype(np.float16)
+
+# Function to save the matrix to a binary file
+def save_matrix_to_bin(file_name, matrix):
+    matrix.tofile(file_name)
+
+# Function to perform matrix multiplication and truncate to specified size
+def truncated_matrix_multiplication(matrix_a, matrix_b, size):
+    truncated_a = matrix_a.flatten()[:size * size].reshape(size, size)
+    truncated_b = matrix_b.flatten()[:size * size].reshape(size, size)
+    result = np.matmul(truncated_a, truncated_b)
+    return result.astype(np.float16)
+
+# Generate the 512x512 matrices
+size = 512
+matrix_a = generate_fp16_matrix(size)
+matrix_b = generate_fp16_matrix(size)
+
+# Save the operand matrices to binary files
+# save_matrix_to_bin("input.a.bin", matrix_a)
+# save_matrix_to_bin("input.b.bin", matrix_b)
+
+# Generate and save the reference matrices for 128x128, 256x256, and 512x512 sizes
+sizes = [128, 256, 512]
+for s in sizes:
+    ref_matrix = truncated_matrix_multiplication(matrix_a, matrix_b, s)
+    print(ref_matrix)
+    # save_matrix_to_bin(f"ref{s}.bin", ref_matrix)
+
+print("All files generated successfully.")
+