Intel's Vector Neural Network Instruction (VNNI) provides higher efficiency on calculating dense linear algebra (DLA) computations than conventional SIMD instructions. However, existing auto-vectorizers frequently deliver suboptimal utilization of VNNI by either failing to recognize VNNI's unique computation pattern at the innermost loops/basic blocks, or producing inferior code through constrained and rudimentary peephole optimizations/pattern matching techniques. Auto-tuning frameworks might generate proficient code but are hampered by the necessity for sophisticated pattern templates and extensive search processes.