Apple Neural Engine
Vendor: Apple
Category: NPU Neural Processor
Architecture: Neural Engine (ANE)
Introduction
Apple A-series and M-series chips with built-in Neural Engine (ANE), designed specifically for machine learning inference acceleration. Features 16-core architecture with up to 38 TOPS compute. Accessed through the Core ML framework, supporting model compression and deployment.
Specifications
| Model | Compute | Memory | Interface | TDP | Process |
|---|---|---|---|---|---|
| A18 Pro ANE | 38 TOPS (INT8) | Unified memory | Integrated SoC | 6W | N3E (3nm) |
| M4 ANE | 38 TOPS (INT8) | Unified memory | Integrated SoC | 10W | N3E (3nm) |
| M3 ANE | 18 TOPS (INT8) | Unified memory | Integrated SoC | 10W | N3B (3nm) |
Official Website
Driver Downloads
macOS
Related Documentation
- Core ML Documentation
- Neural Engine Performance Guide
- Create ML Model Training
- mlx (Apple ML Framework)
OS Support
| Windows | Linux | macOS | Android |
|---|---|---|---|
| ❌ | ❌ | ✅ | ❌ |
Version History
| Version | Release Date | Description |
|---|---|---|
| Core ML 7 | 2024 | M4/A18 Pro, enhanced Transformer models |
| Core ML 6 | 2023 | M3/A17 Pro support |
Performance Benchmarks
| Model | Task | Performance Metric |
|---|---|---|
| A18 Pro ANE | Core ML Inference | 38 TOPS |
| M4 ANE | Core ML Inference | 38 TOPS |
| M4 ANE | Stable Diffusion (Core ML) | ~5s/img |
Pricing
| Model | Reference Price | Notes |
|---|---|---|
| ANE | Provided with Apple SoC | Built into iPhone/iPad/Mac |
Quick Installation
macOS / iOS
ANE is automatically invoked through the Core ML framework. No additional installation required.
# Install coremltools for model conversion
pip install coremltools
Code Examples
Python (Core ML)
import coremltools as ct
import numpy as np
# Convert PyTorch model to Core ML
traced_model = torch.jit.trace(model, example_input)
mlmodel = ct.convert(traced_model, inputs=[ct.TensorType(name="input", shape=example_input.shape)])
mlmodel.save("Model.mlpackage")
# Inference using Apple Neural Engine
import coremltools as ct
model = ct.models.MLModel("Model.mlpackage")
predictions = model.predict({"input": input_array})
Architecture Highlights
- Neural Engine (ANE): Apple dedicated neural network accelerator, operating independently of CPU and GPU with ultra-low power
- Core ML Optimization: Models automatically routed to ANE execution, supporting neural networks and traditional ML
- Unified Memory: ANE shares memory with CPU/GPU, zero-copy model loading
Model Compatibility
| Model/Framework | Support | Notes |
|---|---|---|
| Core ML | ✅ Native | Best support |
| PyTorch | ✅ Convert | coremltools conversion |
| TensorFlow | ✅ Convert | coremltools conversion |
| Stable Diffusion | ✅ | Core ML version |
| Small LLMs | ✅ | Sideloading support |
Related Products
If you're evaluating alternatives, the following products may also fit your scenario:
- AMD Ryzen AI NPU — AMD (NPU Neural Processor)
- Intel NPU (Neural Processing Unit) — Intel (NPU Neural Processor)
- Qualcomm Hexagon NPU — Qualcomm (NPU Neural Processor)
- MediaTek NeuroPilot — MediaTek (NPU Neural Processor)
- Huawei Ascend — Huawei (NPU Neural Processor)
- NVIDIA GPU / CUDA — NVIDIA (GPU Graphics Processor)