---
license: mit
pretty_name: ANE Rooflines
tags:
- benchmark
- apple-neural-engine
- apple-silicon
- roofline
- hardware
size_categories:
- n<1K
configs:
- config_name: default
  data_files: rooflines.json
---

# ANE Rooflines

Cross-Apple-Silicon performance and fp16-correctness measurements for the Apple
Neural Engine (ANE), collected with [ANEForge](https://github.com/sbryngelson/ANEForge).
Each row is one machine (grouped by hardware hash; identical silicon in different
chassis stays distinct by model identifier).

**See it charted:** the [ANE leaderboard](https://huggingface.co/spaces/aneforge/ane-leaderboard)
ranks these machines by peak GEMM, perf-per-watt, and decode throughput.

These are **community-contributed submissions** mirrored from the public
[`bench/results/rooflines/`](https://github.com/sbryngelson/ANEForge/tree/main/bench/results/rooflines)
in the repo. The full per-size sweeps live in `raw/`; `rooflines.json` is the flattened
headline table; `ROOFLINES.md` is the human-readable version.

```python
from datasets import load_dataset
ds = load_dataset("aneforge/ane-rooflines")   # the flattened headline table
```

## Columns

| column | meaning |
| --- | --- |
| `chip`, `model_identifier`, `hardware_hash` | machine identity |
| `p_cores`, `e_cores`, `gpu_cores`, `ram_gb` | CPU perf/efficiency cores, GPU cores, unified memory |
| `macos_version`, `macos_build`, `aneforge_version` | software the run was recorded under |
| `power` | `ac`, `ac (high-power)`, or `battery` at run time |
| `contributor` | GitHub handle who submitted the run |
| `peak_fp16_gemm_tflops` | **headline compute** peak (measured on every machine) |
| `bandwidth_gbps`, `ridge_flop_per_byte` | streaming bandwidth and the ridge point |
| `peak_perf_per_w_gflops` | peak GFLOP/s per watt |
| `decode_tok_s` | single-stream LLM decode throughput |
| `matmul_inf_cliff`, `slice_x16_cliff`, `reduce_exact_sum` | fp16 correctness cliffs (magnitudes where the engine silently returns a wrong answer) |
| `timestamp_utc` | when the run was recorded |

## Reading notes

- **`peak_fp16_gemm_tflops` is the most robust cross-chip number.** Bandwidth/ridge come
  from a streaming sweep and are more dispatch-overhead-sensitive on smaller/older parts,
  so treat them as indicative.
- **`decode_tok_s` currently reports only on A16+ (e.g. M5).** The decode benchmark's
  32000-vocab head exceeded the 16384 max matmul dimension on the A13-A15 families; a
  tiled head fixes this and the older machines re-run to populate it. Blank means the run
  predates the tiled head, not that the chip cannot decode.
- **Correctness cliffs are magnitude thresholds, independent of clock**, so they are valid
  even on battery. `matmul` ~ `fp16_max/2` (~32752); `slice` clamps `|value|>4094` on
  pre-A16 parts and is exact on A16+; `reduce` is bit-exact for integer sums up to 2048.

## Contribute your chip

Run the suite on any Apple Silicon Mac and open a PR:

```sh
PYTHONPATH=. python3 bench/roofline_suite.py --contributor <your-gh-handle>
```

See the [roofline drive](https://github.com/sbryngelson/ANEForge/issues/137). More chip
generations sharpen the per-family map.

## Cite

> Bryngelson, S. H. *ANEForge: Python for direct computation on the Apple Neural Engine.* arXiv:2606.17090 (2026).