Update config

compile_config/network_config_1core.yaml

@@ -0,0 +1,8 @@
+network_configuration:
+  retinanet-9:
+    optimization_options:
+      - -enable_dma_fusion
+      - -enable_stu_multi_channel
+      - -stu_multi_channel_config 0x15212420
+      - -stu_weight
+      - -distributed_coef_stu

int8/benchmarks/x5h_mwmx_npu_apm80_1core.yaml

@@ -44,6 +44,7 @@ power:
 # Exact commands verified against the NNAC "Getting Started" chapter. Rendered
 # by the AI-Dashboard in place of the generic placeholder flow — see
 # downloadRunHTML() / parse_reproduce() in AI-Dashboard/app.js.
+# Network config verified against the internal model_zoo compile config for this model's 1-core slice.
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -52,11 +53,11 @@ reproduce:
         (huggingface_hub) and the NNAC toolchain installed, e.g.
         `source nnac_venv/bin/activate` -- path depends on your toolchain install.
       kind: note
-    - title: Download the ONNX model
-      command: hf download Renesas/RetinaNet-R101-ONNX --repo-type=model --include "fp32/*" --local-dir ./RetinaNet-R101-ONNX-fp32
+    - 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: 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 1
+        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 1 --network-config ./RetinaNet-R101-ONNX-fp32/compile_config/network_config_1core.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_3core.yaml

@@ -41,11 +41,10 @@ memory:
 power:
   avg_w: null
 
-# UNCONFIRMED (segmentation config): this repo's segmentation_json compile_config
-# (compile_config/RetinaNet_l2seg.json, see the 12-core reproduce block) is
-# specific to the 12-core build; no 3-core segmentation config is bundled, so
-# the steps below compile without one — a best-effort single-cluster flow that
-# may not reproduce the exact benchmarked 3-core latency.
+# Exact commands verified against the NNAC "Getting Started" chapter, plus the
+# network config verified against the internal model_zoo compile config for
+# this model's 3-core slice (identical segmentation config to the 3/4/6/12-core
+# slices, see compile_config/RetinaNet_l2seg.json).
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -54,11 +53,11 @@ reproduce:
         (huggingface_hub) and the NNAC toolchain installed, e.g.
         `source nnac_venv/bin/activate` -- path depends on your toolchain install.
       kind: note
-    - title: Download the ONNX model
-      command: hf download Renesas/RetinaNet-R101-ONNX --repo-type=model --include "fp32/*" --local-dir ./RetinaNet-R101-ONNX-fp32
+    - 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: 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 3
+        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 3 --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_4core.yaml

@@ -41,11 +41,10 @@ memory:
 power:
   avg_w: null
 
-# UNCONFIRMED (segmentation config): this repo's segmentation_json compile_config
-# (compile_config/RetinaNet_l2seg.json, see the 12-core reproduce block) is
-# specific to the 12-core build; no 4-core segmentation config is bundled, so
-# the steps below compile without one — a best-effort single-cluster flow that
-# may not reproduce the exact benchmarked 4-core latency.
+# Exact commands verified against the NNAC "Getting Started" chapter, plus the
+# network config verified against the internal model_zoo compile config for
+# this model's 4-core slice (identical segmentation config to the 3/4/6/12-core
+# slices, see compile_config/RetinaNet_l2seg.json).
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -54,11 +53,11 @@ reproduce:
         (huggingface_hub) and the NNAC toolchain installed, e.g.
         `source nnac_venv/bin/activate` -- path depends on your toolchain install.
       kind: note
-    - title: Download the ONNX model
-      command: hf download Renesas/RetinaNet-R101-ONNX --repo-type=model --include "fp32/*" --local-dir ./RetinaNet-R101-ONNX-fp32
+    - 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: 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 4
+        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 4 --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_6core.yaml

@@ -41,11 +41,10 @@ memory:
 power:
   avg_w: null
 
-# UNCONFIRMED (segmentation config): this repo's segmentation_json compile_config
-# (compile_config/RetinaNet_l2seg.json, see the 12-core reproduce block) is
-# specific to the 12-core build; no 6-core segmentation config is bundled, so
-# the steps below compile without one — a best-effort single-cluster flow that
-# may not reproduce the exact benchmarked 6-core latency.
+# Exact commands verified against the NNAC "Getting Started" chapter, plus the
+# network config verified against the internal model_zoo compile config for
+# this model's 6-core slice (identical segmentation config to the 3/4/6/12-core
+# slices, see compile_config/RetinaNet_l2seg.json).
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -54,11 +53,11 @@ reproduce:
         (huggingface_hub) and the NNAC toolchain installed, e.g.
         `source nnac_venv/bin/activate` -- path depends on your toolchain install.
       kind: note
-    - title: Download the ONNX model
-      command: hf download Renesas/RetinaNet-R101-ONNX --repo-type=model --include "fp32/*" --local-dir ./RetinaNet-R101-ONNX-fp32
+    - 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: 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 6
+        python3 nnac_frontend/legalize.py -d binary/nnx ./RetinaNet-R101-ONNX-fp32/fp32/retinanet-9.onnx --num-core 6 --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