[Backend][Relax] Add NPU BYOC backend tutorial with architectural concepts

This commit introduces a vendor-neutral NPU backend that demonstrates
architectural patterns common across Neural Processing Units.

The implementation covers key NPU concepts including multi-tier memory
hierarchy management, automatic tiling for large tensors, quantization
handling, and specialized execution engines. It shows how NPUs manage
memory across different tiers (L0/L1/L2/L3), tile operations to fit
in on-chip SRAM, and dispatch operations to dedicated compute units.

This serves as an educational template for developers creating NPU
backends, demonstrating BYOC integration while teaching NPU-specific
optimization strategies. Uses CPU emulation for testing without
requiring actual NPU hardware.

Addresses feedback from #18201 requesting generic NPU BYOC tutorials.

Author: Aristide021

python/tvm/relax/backend/contrib/example_npu/__init__.py

@@ -0,0 +1,31 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Example NPU Backend for BYOC Integration
+
+This module provides an educational example of how to implement
+a custom NPU backend in TVM using the Bring Your Own Codegen (BYOC)
+framework. It demonstrates key NPU architectural concepts including
+memory hierarchy, tiling, quantization, and operation fusion.
+
+The patterns module registers all supported NPU operations and their
+constraints, making them available for graph partitioning.
+"""
+
+from . import patterns  # noqa: F401
+
+__all__ = ["patterns"]