Apple’s neural engine is specially designed for machine learning and artificial intelligence (AI) workloads. The neural engine of the iPhone always tries to be more unique with its technology than the other competitors in the market. The technology of the iPhone is designed to perform mathematical operations and matrix multiplication. The apple neural engine is much faster than the general purpose CPUs and GPUs in terms of speed and energy efficiency. This technology of the Apple neural engine was introduced first for the iPhone X in 2017, and since then it has been a feature of all new iPhones, iPads, Macs, and Apple TVs. The apple M1 and M2 chips, which are found in the latest Macs and iPad pros, also included in apple neural engine.
“What does the neural engine actually do?”
This is the main issue which I wanted to solve within this article and you can explore more about Neural engine vs GPU, Neural engine vs CPU, difference between GPU and neural engine, and more and more about neural engine.
According to my research about neural engines for a few days to find out how exactly this device might be beneficial for humans, I could find more advantages that would help people with their daily activities and studies. Commonly, we can find this hardware component in devices like smart phones, tablets, games, automotive systems, healthcare, natural language processing, voice assistants, and in data centers. When we are talking about neural engines, we can’t omit the word “apple neural engine”. Because the Apple Company has upside-down technology with the help of a neural engine,
The 2017 iPhone X, Apple’s flagship model, included the first version of the Apple neural engine. It effectively ran on-device ML features like face Id and memoji of 0.6 teraflops in half precision floating point data format. In 2021, the 16-core Apple neural engine’s fifth generation can process information of a rate of 15.8 TF lops, which is 26 times faster than the original. From a small number of Apple applications to a large number of applications from both apple and the developer community, The ANE has seen a steady increase use since 2017. In 2017, the neural engine was limited to the iPhone. However, it became available for iPad with the A12 chip and mac with the M1 chip starting in 2017.
When we think about the future of Apple Neural Engine, it is difficult to imagine how it would be in the future. The Apple neural engine has a bright future. Apple is making significant investments with this technology, which is already greatly impacting the functionality and performance of Apple products. When I visit Apple’s website to get more information about the future of iPhone. I could get to know that Apple is constantly designing more powerful and energy efficiency devices including neural engine. In future, the devices with neural engine will be capable of handling a larger variety of machine learning tasks. Because, Apple is working to increase the versatility of the neural engine. They try to handle large variety of machine learning tasks including once that the CPU and GPU currently handle for other parts of the system.
Many functions on apple devices are powered by the apple neural engine, such as,
- Face ID
- Real time translation in the Translate app
- Image recognition and object tracking in the Photos app
- Fraud detection and security features
- Animoji Memoji
- Smart HDR and Night mode on iPhone cameras
- Personalized recommendations
- Recommendations in the Music and TV apps
This specialized technology offers a number of advantages than the traditional CPUs and GPUs. Neural engine show high performance when working. Because of having specialized hardware system, the device together with neural engines performs well and they are much faster than the general purpose processors like CPUs and GPUs.
The neural engines have been customized for the unique mathematical operations like activation functions, convolutions, and matrix multiplication. Commonly neural engines have the ability of enhancing the data privacy, when using any device.
The privacy of data is the most important thing. So, neural engines provide the security for sensitive data without sending to remote servers. This hardware component have specialized operations and they usually enable precision necessities and custom operations allowing the execution of artificial intelligence (AI) models that are exactly calibrated.
The goal of promoting neural engines in to smart phones or tablets is to maximize the efficiency of artificial intelligence and machine learning calculations. These are the specialized processors designed to perform tasks like natural language processing, speech and image recognition.
Image signal processor is the other important feature in the other important feature in smart phones with neural engine which I could find within my research. This processor can handle functions like image capturing, processing and optimizing images.
Video processing units in smart phones are done operations like video encoding and decoding as well as video playback and recording. Audio processing units also can see in mobile phones with neural engine. It can handles audio playback, noise cancellation and video signal processing for the audio signal processing for the voice assistant and phone calls. The GPS processor is important for location aware and navigational applications as it is designed specifically to handle GPS and location based services.
As we discussed earlier, the neural engine offers more advantages when performing. However the devices together with the neural engines show some disadvantages too. Neural engines are designed for specialized AI workloads. They might not be flexible as general purpose devices. They are specific for AI tasks and less adoptable than the general purpose processors like GPUs and CPUs which can handle a number of applications.
The technology of the neural engine rapidly enhancing. Because of that the market for the devices with neural engine is outdating quickly and price of the devices with neural engine is so high than the normal devices.
Difference between GPU and neural engine
We have identified some key differences between the GPU and neural engine.
|Feature||Graphics Processing Unit (GPU)||Neural engine|
|Purpose||Graphic processing||Machine learning|
|Strengths||Speed, Parallelism, versatility||Efficiency, energy, latency|
|Weaknesses||Less energy efficient, more expensive, higher latency||Less versatile|
|Typical users||Training and running large machine learning models, graphics rendering, scientific computing.||On device machine learning , mobile applications|
|Examples||NVIDIA GeForce RTX 3090, AMD Radeon RX 6900 XT||Apple A16 Bionic Neural Engine, Google Tensor processing Unit. (TPU)|
What is the Apple neural engine Used for?
As I think we will be able to see even more innovative and transformative AI applications in future. Neural engines are getting stronger and developing quickly. For Apple Company, neural engine is the main technology to maintain its competitive advantages in the market of smart phone. Apple’s competitors such as Samsung and Google are also introducing more and more technology. The brand of Apple is playing the main role in the market with the help of the technology of neural engine. And also this hardware component is the main reason why Apple iPhones are the best smart phone on the market. The neural engine is helps developers to create new and innovative products and services that were not possible before, by increasing the power and efficiency of machine learning. Within my research I could find some specific examples of the importance of apple neural engine.
- Faster and more accurate face ID.
In the Apple’s facial recognition system, the neural engine is used to power Face ID.
The Apple neural engine is performing complex machine learning calculations in real time to make Face ID faster and more accurate.
- Improved iPhone camera performance.
The neural engine powers many of the iPhone’s camera feature including smart HDR , Night mode, and portrait mode. This technology helps to improve the quality of these features by performing complex image processing tasks.
- Longer battry life
By improving performance for various tasks, the neural engine contributes to the I phone’s longer battry life. When the iPhone is not in use, the neural engine can be utilized to power low-power machine learning learning models.
- Real time translation
Real-time language translation between languages is provided by the translate app for ios and mac os that uses machine learning.
- New and innovative apps and services
The neural engine is helping developers to create new innovative apps and services that were not possible earlier. As an example, argumentated reality. Experience object tracking, video analysis fraud detection and medical diagnosis.
Apple is heavily investing this technology for iPhone. New security features that can more accurately identify and stop fraud and other malicious activity could be created using the neural engine in future. Not only that, we will be able to see now creative features for photography and videography. It will be developed with the neural engine which will be used to enhance the quality of images and videos captured with iPhone camera. The neural engine will be used for power augmented reality and virtual reality experience that are more lifelike and immersive than before. Amount of unified memory in Apple products, it’s an amazing convention that sets the stage for more, Superior neural engine and presents a fresh challenge for Apple’s neural engine.
I got more details through the Apple’s website to complete my research and for the people with no prior knowledge of the technology of a neural engine and specially about features of Apple’s neural engine, my article provides as an excellent reference.