| --- |
| 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). |
| |
