Apple Inc. first introduced the proprietary neural network hardware it called Neural Engine with the launch of the A11 Bionic and the iPhone X, iPhone 8, and iPhone 8 Plus on 12 September 2017 and the more recent A13 Bionic. For starters, the Neural Engine is a dedicated hardware and a coprocessor found within the A-Series Bionic and Apple M series of systems-on-chips designed by the company. It essentially equips devices such as the iPhone and iPad with native or localized artificial intelligence and machine learning capabilities.
What is the Neural Engine of Apple: Features, Applications, and Benefits
One of the key selling points of the A-Series of microprocessors since the A11 Bionic is the built-in machine learning capabilities. As a backgrounder, machine learning is a specific application of artificial intelligence that allows a computer to learn without being explicitly programmed to automatically process a large amount of data, understand patterns, and provide corresponding predictions or interferences.
Apple has been utilizing machine learning to power some of its products and deliver some of its notable services include the virtual assistant application Siri and the content discovery functionalities of the App Store and Apple Music.
However, before the A11 Bionic, Apple devices such as the iPhone and iPad do machine learning processing via the cloud. For example, Siri works almost exclusively with an Internet connection to process inputs from speech or text and deliver the necessary requests from the users. The integration of the Neural Engine within a device means that a portion of machine learning processes can now be done locally.
The Neural Engine from Apple has several features and applications. Through this dedicated neural network hardware, equipped devices can process specific machine learning algorithms, thus increasing their functionalities.
Some of the notable applications of localized machine learning processing are the Face ID security technology and animated emoji features that first appeared on the iPhone X. Note that these features depend primarily on facial recognition. The Neural Engine enables the device to have a dedicated hardware for processing real-time and stored images.
It is also worth mentioning the fact that newer generations of iPhones have now augmented reality features that also depend on image processing. Again, the animated emoji feature of these devices is also based on augmented reality.
Other applications of the dedicated neural network hardware include speech processing and other user-input processing. To illustrate better, it optimizes the functionality of Siri, which initially depended on recognizing and associating speech or other inputs with historical data that are available via the cloud. However, the Neural Engine allows the localized processing of these data.
The introduction of the iPhone 11 line of smartphones in September 2019 also marked another key application of Neural Engine. Apple introduced a new feature called Deep Fusion, which is based on computational photography, as well as Night Mode, which also uses machine learning to improve image quality taken at low light.
Devices equipped with the Neural Engine can essentially process images and speech while learning to become better at doing so. This means faster and more accurate augmented reality applications, image processing, and speech recognition.
Another benefit of the Neural Engine is that it allows for more effective and efficient use of hardware. To be more specific, this hardware uses machine learning to promote efficiency in the operation of the CPUs. It can gradually learn how to become more efficient in processing and consuming power based on historical data obtained from day-to-day user-to-device interaction.
A Concise Snapshot of the Features, Applications, and Benefits of the Neural Engine
• The Neural Engine from Apple is a neural network hardware integrated within the A-Series line of microprocessors since the A11 Bionic.
• A neural network hardware is an artificial intelligence accelerator designed for AI applications to include machine learning, as well as data processing for a more specific image and speech processing.
• The central feature of the Neutral Engine is localized machine learning capabilities that allows a device to perform native processing of machine learning algorithms. Hence, the benefits of the hardware center on faster and efficient device performance.
• Notable applications of the hardware include optimizing the speech and input recognition capabilities of Siri, powering the Face ID security technology, delivering augmented reality features such as animated emoji, and better camera performance.
• There is also a notable application on smartphone photography. The hardware powers the new Deep Fusion and Night Mode features first introduced in iPhone 11. Note that these features are based on the concept of computational photography.
• Another application and thus, benefits of the Neural Engine centers on being able to learn how to become more efficient in processing and consuming power based on historical data obtained from day-to-day user-to-device interaction.