int8/benchmarks/x5h_mwmx_npu_apm80_4core.yaml

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
  toolchain_version: "MWMX SDK v4.35.0"
  format: onnx
  execution_provider: npu
  execution_precision: int8

configuration:
  npu_instances: 1
  npu_cores_per_instance: 4
  npu_freq_mhz: 850

benchmark:
  type: hil
  parameters:
    batch_size: 1
    input_resolution: [1, 3, 512, 1024]   # explicit in the source checkpoint name (512x1024, Cityscapes)

performance:
  fps: null
  latency: 230.27        # synced to mwmx2.2 model_list.html (2026-09-22); no prior local measurement for this core count
  # of a 4-way split model ("custom_seg_split_4_split_2") — not full end-to-end latency.

metrics:
  accuracy: null
  top5_accuracy: null

memory:
  peak_mb: null

power:
  avg_w: null

# 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.
# CAVEAT: this artifact is segment "split_2" of a 4-way split network (see
# README) — these steps reproduce only this segment's latency, not an
# end-to-end DeepLabV3+ result. The network config below is required for this
# model — there is no working default.
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/DeepLabV3Plus-R50-ONNX --repo-type model --include "fp32/*" "compile_config/*" --local-dir ./DeepLabV3Plus-R50-ONNX-fp32
    - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
      command: |
        python3 nnac_frontend/legalize.py -d binary/nnx ./DeepLabV3Plus-R50-ONNX-fp32/fp32/deeplabv3plus_r50_oss_sim_inf.onnx --num-core 4 --network-config ./DeepLabV3Plus-R50-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, 4 AI cores)
      command: |
        cd binary
        ./host_app ./arc_prog_npus ./nnx/deeplabv3plus_r50_oss_sim_inf
      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.
  notes: This is one segment (split_2 of 4) of the full segmentation pipeline — not whole-model latency.