ANEForge: Python for direct computation on the Apple Neural Engine

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[2606.17090] ANEForge: Python for direct computation on the Apple Neural Engine

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Computer Science > Programming Languages

arXiv:2606.17090 (cs)

[Submitted on 12 Jun 2026]

Title:ANEForge: Python for direct computation on the Apple Neural Engine

Authors:Spencer H. Bryngelson<br>View a PDF of the paper titled ANEForge: Python for direct computation on the Apple Neural Engine, by Spencer H. Bryngelson

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Abstract:ANEForge is a Python package that programs the Apple Neural Engine (ANE), the fixed-function neural accelerator on every recent Apple device, directly and without CoreML. In production the engine is reachable only through CoreML, which treats it as a scheduling option: no configuration requires the ANE, and a model can silently run on the CPU or GPU instead. ANEForge compiles a lazy tensor graph, built from 58 fused operators and 19 native bridge operators, into a single ANE program. The program is dispatched through the same ANE daemon and kernel-driver stack as Apple's internal framework. Beyond inference, the package reaches the engine's native fused attention, streams int8, int4, and sparse weights, keeps decoder and optimizer state resident across steps, and runs the forward pass, backward pass, and optimizer update of training on the engine. A small fused program completes a call in about 90us, near the engine's 70us per-program dispatch floor, and a pretrained ResNet-18 forward runs end-to-end in 0.33ms. ResNet-18, a sentence encoder, and a Vision Transformer run end-to-end against framework references, and a Stable Diffusion U-Net validates its forward pass. ANEForge targets Apple Silicon under macOS 14 and later. Each release is verified against a recorded macOS and ANE-compiler version.

Comments:<br>8 pages

Subjects:

Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Mathematical Software (cs.MS)

Cite as:<br>arXiv:2606.17090 [cs.PL]

(or<br>arXiv:2606.17090v1 [cs.PL] for this version)

https://doi.org/10.48550/arXiv.2606.17090

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arXiv-issued DOI via DataCite

Submission history<br>From: Spencer Bryngelson [view email]<br>[v1]<br>Fri, 12 Jun 2026 21:52:06 UTC (15 KB)

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