Remove duplicate benchmark

int8/benchmarks/x5h_mwmx_npu.yaml

@@ -1,69 +0,0 @@
-hardware:
-  vendor: renesas
-  chip: rcar-x5h
-  cpu: arm-cortex-a720
-  npu: npx6-48k
-  npu_count: 2
-  npu_cores: 12
-  npu_default_freq_mhz: 1066
-  accelerator:
-    - npu
-
-runtime:
-  engine: mwmx             # Renesas MWMX (Middleware MX) native inference runtime
-  toolchain_version: "MWMX SDK v4.35.0"
-  format: onnx             # input model format (FP32 ONNX)
-  execution_provider: npu
-  execution_precision: int8  # FP32 model is auto-cast to INT8 by the MWMX toolchain
-
-configuration:
-  npu_instances: 1         # single NPU
-  npu_cores_per_instance: 12  # single AI core
-  npu_freq_mhz: 850        # 850 MHz NPU clock frequency
-
-benchmark:
-  type: hil                # Hardware-in-the-loop — measured on physical X5H silicon
-  parameters:
-    batch_size: 1
-    input_resolution: [1, 3, 480, 640]
-
-performance:
-  fps: null             # throughput: 1000 / latency
-  latency: 7.30           # median inference latency (ms) over 1000 warm runs
-
-metrics:
-  accuracy: null
-  top5_accuracy: null
-
-memory:
-  peak_mb: null             # peak NPU memory during inference
-
-power:
-  avg_w: null             # not yet characterized
-
-# 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:
-  steps:
-    - title: Activate the Python environment
-      command: >-
-        Activate the Python virtual environment that has the `hf` CLI
-        (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 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 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
-    - title: Copy the compiled artifact to the board
-      command: Copy ${WORKDIR}/binary/nnx (the working directory from the download/compile steps above) to the board -- method may vary (NFS mount, scp, USB, etc.).
-      kind: note
-    - title: Run on R-Car X5H (single NPU cluster, 12 AI cores)
-      command: |
-        cd binary
-        ./host_app ./arc_prog_npus ./nnx/retinanet-9
-      expected: hash[n] = 0x...(OK) means the run's output matches the reference hash in hash.txt; latency is the NPX execution time reported in cycles and ms.