Cross-Apple-Silicon performance and fp16-correctness measurements for the Apple
Neural Engine (ANE), collected with 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
ranks these machines by peak GEMM, perf-per-watt, and decode throughput.
These are community-contributed submissions mirrored from the public
bench/results/rooflines/
in the repo.… See the full description on the dataset page:
https://huggingface.co/datasets/aneforge/ane-rooflines.
