As technology advances with AI and machine learning, computer processors are becoming more powerful. One major improvement is the Neural Engine, a special feature first introduced by Apple.
Now, it’s showing up in many modern processors. But what exactly is a Neural Engine? How does it function? And why is it so important for the future of computers? This guide will explain everything you need to know.
What is a Neural Engine?
A Neural Engine is a dedicated AI and machine learning processor built into modern CPUs and SoCs (System on Chips). Unlike general-purpose processors, it is optimized to accelerate AI-related tasks such as deep learning, neural network inference, and real-time AI computations.
Key Characteristics of a Neural Engine:
- Optimized for AI Tasks: Unlike CPUs or GPUs, it is specifically designed for neural network processing.
- Power-Efficient: AI computations can be power-hungry, but Neural Engines handle them efficiently.
- Parallel Processing: It can execute multiple AI-related tasks simultaneously.
- Low Latency: It processes real-time AI workloads with minimal delay.
- Integrated AI Acceleration: Works in harmony with CPUs and GPUs for optimized performance.
History, and Evolution of Neural Engines
Early AI Processing
Before the advent of Neural Engines, AI computations were handled primarily by:
CPUs (Central Processing Units) – General-purpose but slow for AI-related computations.
GPUs (Graphics Processing Units) – More efficient for AI but power-hungry.
TPUs (Tensor Processing Units) – Custom AI accelerators from Google designed for cloud-based AI processing.
Apple’s Introduction of the Neural Engine
2017: Apple launched the first Neural Engine in the A11 Bionic chip, integrated into iPhones to power Face ID, Animoji, and real-time AR (Augmented Reality) processing.
2018-Present: Apple continued improving its Neural Engine in A12 to M4 chips, increasing AI performance from 600 billion operations per second in A12 to 38 trillion operations per second in M4.
2023: The Apple M3 Pro and M3 Max chips enhanced on-device AI processing, enabling real-time voice isolation, auto-transcription, and live translations.
Other Companies Adopting Neural Processing Units (NPUs)
Qualcomm: Snapdragon’s Hexagon NPU delivers up to 45 TOPS (trillions of operations per second), used in AI camera processing and real-time voice recognition in Snapdragon 8 Gen 3.
Intel: Introduced AI-optimized Thread Director in Core Ultra series, balancing AI workloads across CPU, GPU, and NPU.
AMD: Launched Ryzen AI in Ryzen 7040 series, providing high-efficiency AI acceleration in Windows laptops.
Google: Tensor Processing Units (TPUs) in Pixel smartphones optimize photography enhancements, speech-to-text, and live captioning with 60% less power consumption than cloud AI models.
How Does a Neural Engine Work?
A Neural Engine operates using dedicated AI cores that efficiently process deep learning models. Here’s how it functions:
1. Data Input & Preprocessing
The Neural Engine receives input data (such as images, voice commands, or text).
It preprocesses the data, converting it into a format suitable for AI model inference (e.g., pixel values for images, spectrograms for voice).
Apple M4 Neural Engine: Capable of 38 Trillion Operations Per Second (TOPS), up from 18 TOPS in M3
Use Case: Face ID on iPhones takes just 1.2 seconds to scan and authenticate, leveraging real-time inference on the Neural Engine.
2. Neural Network Inference
The Neural Engine loads a pre-trained AI model (e.g., a deep learning model for face recognition).
It processes the data through the neural network layers, performing computations like matrix multiplications and activations.
Example: In Face ID, the Neural Engine analyzes facial landmarks in milliseconds and compares them with stored biometric data.
3. Parallelized Processing
Unlike CPUs, which execute tasks sequentially, Neural Engines run multiple AI operations at the same time (parallel computation).
Example: A smartphone camera applies real-time HDR enhancements by processing multiple image frames simultaneously.
4. Matrix Multiplication Acceleration
Deep learning models rely on complex matrix multiplications, which are computationally heavy.
The Neural Engine has dedicated cores that accelerate matrix operations, making AI tasks faster than CPUs and GPUs.
Example: Apple’s Neural Engine in the M4 chip can perform 38 trillion operations per second (TOPS) for AI processing.
5. Power Optimization & Resource Allocation
AI tasks are intelligently assigned to different processing units (CPU, GPU, and Neural Engine) for energy efficiency.
The Neural Engine offloads AI tasks from the CPU, reducing battery drain.
Example: Google Tensor NPU improves voice-to-text performance by 40% while consuming 30% less power compared to CPUs.
6. Output Generation & AI Enhancement
The final processed AI results are converted into a usable format (e.g., enhanced images, voice translations, or video effects).
Example: AI-powered Siri or Google Assistant provides instant, locally processed responses instead of cloud-based processing.
7. Continuous Learning & Updates
AI models running on Neural Engines improve over time through firmware updates and user interactions.
Example: Apple’s Core ML framework updates models periodically, enhancing photo processing and Siri’s voice recognition.
Applications of Neural Engines
The impact of Neural Engines is massive, spanning multiple industries. Here are some real-world applications:
1. AI in Smartphones
- Face ID & Biometrics: Secure facial recognition (Apple Face ID, Android Face Unlock).
- Camera Enhancements: AI-powered photography with improved HDR and night mode.
- Real-time Language Translation: Faster AI-driven speech-to-text and language processing.
Apple’s Face ID processes facial recognition in just 1.2 seconds, leveraging the Neural Engine in A17 Pro for ultra-fast AI inference. Google Pixel 8’s Tensor NPU improves voice recognition speed by 40% compared to traditional CPU processing.
2. AI in Laptops & Desktops
- Windows Copilot & macOS AI Features: Real-time AI assistance.
- Video & Audio Enhancement: AI-powered noise cancellation in calls.
- Intelligent Battery Optimization: Adaptive power management using AI.
MacBook Pro with M3 Max uses the Neural Engine for real-time noise cancellation, reducing background noise by 60% in video calls. Windows 11 AI-powered Copilot leverages Intel’s Core Ultra AI Engine to generate text and code 30% faster than non-AI processors.
3. AI in Gaming
- DLSS (Deep Learning Super Sampling): AI-powered frame upscaling for smoother gameplay.
- AI-driven NPCs & Smart Rendering: Realistic character interactions in games.
NVIDIA DLSS 3.5 upscales gaming graphics using AI, increasing frame rates by up to 2x at 4K resolution. Sony PlayStation 5 AI-assisted ray tracing boosts frame rates by 10%, enhancing real-time reflections and shadows.
4. AI in Cloud Computing & Edge AI
- AI Workloads on Edge Devices: Reducing dependency on cloud processing.
- AI Security & Threat Detection: AI-enhanced cybersecurity in modern systems.
AWS Inferentia (AI Chip for Cloud Computing) speeds up AI model inference by 60% while consuming 50% less power than traditional GPUs. Microsoft Defender AI uses AI-powered threat detection, reducing false positives by 30% while improving malware detection accuracy.
Neural Engine vs. CPU vs. GPU vs. TPU
Feature | Neural Engine | CPU | GPU | TPU (Tensor Processing Unit) |
---|---|---|---|---|
Optimized for AI | ✅ Yes | ❌ No | ✅ Partially | ✅ Yes |
Power Efficient | ✅ Yes | ❌ No | ❌ No | ✅ Yes |
Speed for AI Tasks | ✅ Fast | ❌ Slow | ✅ Fast | ✅ Fast |
Multi-tasking | ✅ High | ✅ Moderate | ✅ High | ❌ Limited |
Processing Type | Matrix & AI acceleration | Serial Processing | Parallel Graphics & Compute Processing | Deep Learning Optimization |
Latency | Ultra-Low | High | Moderate | Low |
Best Use Case | AI-driven tasks | General computing | Graphics & AI | Cloud AI Processing |
Example Products | Apple Neural Engine, Snapdragon Hexagon NPU | Intel Core, AMD Ryzen | NVIDIA RTX, AMD Radeon | Google Cloud TPUs |
Future of Neural Engines
The rapid advancement of Neural Engines is set to redefine the future of AI computing. As AI-driven applications become more complex, Neural Engines will play a pivotal role in delivering faster, more efficient, and smarter computing experiences. Here’s a glimpse into what the future holds:
1. Ultra-Powerful AI Chips
Future processors will feature next-gen Neural Processing Units (NPUs) with trillions of operations per second, enabling devices to run AI models locally without cloud dependency.
Integration of Intel Meteor Lake, AMD Ryzen AI, and Apple M4 chips will push AI acceleration to new heights, making real-time AI tasks seamless.
2. AI-Powered Personal Assistants & Automation
Virtual assistants like Siri AI+, Windows Copilot, and Google Gemini will evolve to become more context-aware, providing personalized and human-like interactions.
AI-driven workflow automation will enhance productivity, from real-time transcription to predictive task management.
3. AI in Every Aspect of Life
AI will drive real-time video editing, AI-generated content, and even automated news reporting, making content creation more efficient than ever.
Neural Engines will power advanced medical diagnostics, enabling instant disease detection through AI-assisted imaging and analysis.
4. The Fusion of Quantum AI & Neural Engines
The future may see Quantum Computing integrated with Neural Engines, delivering exponentially faster AI processing.
This could unlock breakthroughs in drug discovery, cryptography, and AI research, making near-instant computations a reality.
The evolution of Neural Engines is not just about speed and efficiency—it is about transforming the way AI interacts with the world. As we move forward, Neural Engines will become the driving force behind a smarter, AI-empowered future.
Conclusion
The Neural Engine has revolutionized AI processing, enabling devices to perform complex computations efficiently. As AI continues to advance, Neural Engines will play an even bigger role in making AI-driven tasks faster, more efficient, and more accessible. Whether in smartphones, laptops, gaming, or cloud computing, Neural Engines are paving the way for the next generation of AI-powered technology.
Stay ahead of the AI revolution! If you found this guide helpful, share it and let others explore the power of Neural Engines.
FAQs
A Neural Engine is used for AI-driven tasks like facial recognition, speech processing, real-time image enhancement, and AI-powered gaming.
While both can handle AI computations, a Neural Engine is specifically optimized for deep learning inference and matrix calculations, whereas GPUs are designed for graphics rendering and general AI acceleration.
Yes, by offloading AI tasks from power-hungry CPUs and GPUs, Neural Engines reduce energy consumption, leading to longer battery life in mobile devices and laptops.
Yes, Neural Engines contribute to AI-driven enhancements like NVIDIA DLSS, ray tracing optimizations, and real-time physics simulations in gaming.
The future includes more powerful AI chips with higher AI TOPS (Trillions of Operations Per Second), deeper integration with cloud AI, and potential quantum computing synergies for next-gen AI processing.