First Profile#

A guided, no-config first run: from unboxing an Apollo EVB to a per-layer cycle-count table in about five commands.

Before you start#

• Installed heliaPROFILER and the toolchain — hpx doctor shows all required tools as ✓.
• An Ambiq Apollo EVB, powered and connected to your host over its J-Link USB connector (the on-board debug USB port — check your specific EVB's quick-start card if it has more than one USB connector).

1. Confirm the toolchain#

hpx doctor

All required rows should show ✓ (see Installation if not).

2. Find your J-Link probe#

hpx probes list

serial      product                    connection
----------  -------------------------  ----------
801000001  J-Link-OB-Apollo4-CortexM  USB

If nothing is listed, check the USB cable and that the SEGGER J-Link software from Installation is installed.

3. Find your board name#

heliaPROFILER needs a --board value that matches your EVB. List every board it knows about:

hpx boards

Match the silkscreen/part number on your EVB against the Board column (for example apollo510_evb for an Apollo510 EVB). The default board is apollo510_evb — pass --board <name> explicitly if yours is different.

4. Run the profiler#

hpx profile examples/quickstart/kws_model.tflite --board apollo510_evb

With no other flags, heliaPROFILER:

Progress prints stage-by-stage. The first run takes longer (toolchain download for heliaRT, NSX configure, build, flash, capture); subsequent runs are faster because the heliaRT distribution and build artifacts are cached.

5. Look at the result#

Three files matter on your first run:

results/
├── summary.json         ← read this first
├── profile_results.csv  ← per-layer breakdown for spreadsheets
└── run_metadata.json    ← what config was actually used

Open summary.json:

{
  "engine": "helia-rt",
  "layers": 13,
  "total_cycles": 123456.0,
  "overflow_detected": false,
  "top_layers": [
    {"index": 1, "op": "CONV_2D", "cycles": 34567},
    {"index": 4, "op": "DEPTHWISE_CONV_2D", "cycles": 23456}
  ],
  "memory": { "arena_size": 131072, "allocated_arena": 29780 }
}

Three things to read off:

• total_cycles — the headline cost of one inference, averaged across iterations.
• top_layers — which ops dominate runtime. Optimization effort goes here.
• memory.allocated_arena — how much RAM TFLM actually used. If it's much smaller than arena_size, you can shrink the arena.

6. View the per-layer table#

profile_results.csv has one row per layer, one column per counter. See Output & Reports for the full column reference and what each counter means. The same data is also rendered as Model Explorer overlays under results/model_explorer/.

7. Keep going#

Now that you've got a baseline:

• Repeatable runs? → Quick Start with a config file
• Different model placement? → Memory Placement
• Try heliaAOT? → Inference Engines
• Faster build with armclang? → Toolchains
• Add power numbers? → Power Measurement

Troubleshooting the first run#