Move network_config in dedicated file

compile_config/network_config.yaml

@@ -0,0 +1,7 @@
+optimization_options:
+  - -segmentation_json ./RetinaNet-R101-ONNX-fp32/compile_config/RetinaNet_l2seg.json
+  - -enable_dma_fusion
+  - -enable_stu_multi_channel
+  - -stu_multi_channel_config 0x15212420
+  - -stu_weight
+  - -distributed_coef_stu

int8/benchmarks/x5h_mwmx_npu.yaml

@@ -41,17 +41,6 @@ memory:
 power:
   avg_w: null             # not yet characterized
 
-network_configuration:
-  key: retinanet_9
-  entry_yaml: |
-    optimization_options:
-      - -segmentation_json ./RetinaNet-R101-ONNX-fp32/compile_config/RetinaNet_l2seg.json
-      - -enable_dma_fusion
-      - -enable_stu_multi_channel
-      - -stu_multi_channel_config 0x15212420
-      - -stu_weight
-      - -distributed_coef_stu
-
 # Same config (1 instance, 12 cores, 850 MHz) as x5h_mwmx_npu_apm80_12core.yaml
 # — reusing its verified reproduce steps rather than re-deriving them.
 reproduce:
@@ -64,20 +53,9 @@ reproduce:
       kind: note
     - title: Download the ONNX model and compile config
       command: hf download Renesas/RetinaNet-R101-ONNX --repo-type model --include "fp32/*" "compile_config/*" --local-dir ./RetinaNet-R101-ONNX-fp32
-    - title: "Insert this model's entry into network_configuration: in nnac_config/config.yaml"
-      command: |
-        retinanet_9:
-          optimization_options:
-            - -segmentation_json ./RetinaNet-R101-ONNX-fp32/compile_config/RetinaNet_l2seg.json
-            - -enable_dma_fusion
-            - -enable_stu_multi_channel
-            - -stu_multi_channel_config 0x15212420
-            - -stu_weight
-            - -distributed_coef_stu
-      kind: snippet
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 12
+        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 12 --network-config ./RetinaNet-R101-ONNX-fp32/compile_config/network_config.yaml
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note

int8/benchmarks/x5h_mwmx_npu_apm80_12core.yaml

@@ -41,20 +41,9 @@ memory:
 power:
   avg_w: null
 
-network_configuration:
-  key: retinanet_9
-  entry_yaml: |
-    optimization_options:
-      - -segmentation_json ./RetinaNet-R101-ONNX-fp32/compile_config/RetinaNet_l2seg.json
-      - -enable_dma_fusion
-      - -enable_stu_multi_channel
-      - -stu_multi_channel_config 0x15212420
-      - -stu_weight
-      - -distributed_coef_stu
-
 # Exact commands verified against the NNAC "Getting Started" chapter, plus the
-# network_configuration block verified against the internal model_zoo compile
-# config for this model's 12-core slice (see
+# network config verified against the internal model_zoo compile config for
+# this model's 12-core slice (see
 # compile_config/RetinaNet_l2seg.json). Rendered by the AI-Dashboard in place
 # of the generic placeholder flow — see downloadRunHTML() / parse_reproduce().
 reproduce:
@@ -67,20 +56,9 @@ reproduce:
       kind: note
     - title: Download the ONNX model and compile config
       command: hf download Renesas/RetinaNet-R101-ONNX --repo-type model --include "fp32/*" "compile_config/*" --local-dir ./RetinaNet-R101-ONNX-fp32
-    - title: "Insert this model's entry into network_configuration: in nnac_config/config.yaml"
-      command: |
-        retinanet_9:
-          optimization_options:
-            - -segmentation_json ./RetinaNet-R101-ONNX-fp32/compile_config/RetinaNet_l2seg.json
-            - -enable_dma_fusion
-            - -enable_stu_multi_channel
-            - -stu_multi_channel_config 0x15212420
-            - -stu_weight
-            - -distributed_coef_stu
-      kind: snippet
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 12
+        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 12 --network-config ./RetinaNet-R101-ONNX-fp32/compile_config/network_config.yaml
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note