A neural engine is a specialized processor designed to accelerate artificial intelligence (AI) and machine learning (ML) tasks directly within electronic devices. It acts as a dedicated component, offloading complex AI calculations from the device’s main processor. The fundamental purpose of a neural engine is to enable faster, more efficient, and often more private AI processing on a device.
Specialized AI Processing
A neural engine functions by performing large-scale matrix multiplications and other arithmetic operations fundamental to neural network computations. These operations, such as multiplication and accumulation, are executed in parallel across many specialized cores within the engine. This parallel processing capability allows the neural engine to handle vast amounts of data simultaneously, which is characteristic of AI workloads like image classification and natural language processing.
Neural networks consist of interconnected nodes, or “neurons,” arranged in layers, with connections between them having associated weights. The neural engine’s architecture is optimized specifically for these types of calculations, making it highly efficient for inference—the process of applying a trained AI model to new data to make predictions.
Beyond CPU and GPU
While CPUs (Central Processing Units) handle general computing tasks and GPUs (Graphics Processing Units) excel at parallel graphics rendering, the neural engine is purpose-built for AI/ML workloads. CPUs are designed for sequential processing and a wide range of instructions, making them versatile but less efficient for the highly parallel nature of neural network computations. GPUs, with their hundreds or thousands of cores, are proficient at parallel processing for graphics and can also accelerate some AI tasks, particularly the training of deep neural networks.
This specialization allows neural engines to offer superior efficiency, speed, and lower power consumption for AI-specific computations compared to general-purpose CPUs and even GPUs for certain inference tasks.
Everyday Applications
Neural engines power many AI applications we use daily on our devices.
- Facial recognition systems, such as Face ID, rely on the neural engine to process biometric data quickly and securely on the device, ensuring instantaneous unlocking.
- Voice assistants like Siri, Google Assistant, and Alexa utilize the neural engine for real-time speech recognition and natural language processing, allowing them to understand complex queries and generate human-like responses.
- Computational photography features in smartphones, including Smart HDR and Night Mode, leverage the neural engine to process vast amounts of sensor data. This enables real-time image enhancements, noise reduction, and the application of effects like bokeh, leading to higher quality photos and videos.
- The neural engine also supports personalized recommendations by processing user data locally, enhancing privacy.
- Real-time language translation and augmented reality applications, such as object tracking and scene recognition through a smartphone’s camera, are further examples of tasks benefiting from the neural engine’s on-device processing capabilities.
Driving On-Device Intelligence
Neural engines enable more advanced, responsive, and private on-device intelligence. By performing complex AI tasks locally, these engines reduce the need for data to be sent to cloud servers, which minimizes latency and enhances user experience. This local processing also improves privacy by keeping sensitive personal data on the device, reducing the risk of data breaches during transmission.
Neural engines contribute to energy efficiency by executing AI workloads with lower power consumption, extending battery life in mobile devices. This shift towards on-device AI allows devices to operate more independently, providing seamless functionality even without a constant internet connection. The integration of neural engines paves the way for more sophisticated and personalized features directly on our devices, enhancing their responsiveness and overall utility.