Apple’s Impressive Neural Engine!
Apple’s Best Addition In A Decade… Explained!!
Introduction
Machine learning and AI are changing the world! Everyone knows that at this point, and everyone is trying their best to use this to their advantage, and this makes perfect sense.
Of course, with machine learning playing such an integral role in so many parts of our lives, Apple could not wait to make use of that and decided to play a crucial role in this technology that is currently changing everything related to tech. They decided to not only invest in AI, but Apple decided a few years back, that every single device released by Apple should feature a dedicated engine that can handle everything related to AI, and Apple decided to name it the “Neural Engine”. It is a pretty clever name.
This engine is not like any other! It is a huge step for Apple and one that Apple knew was necessary. Apple decided that in a few years, they won’t have any other option but to accept AI and integrate it into every device of theirs, so they decided to play the game early and get a head start, and this is when the Neural Engine came to the market back in 2017, more than 6 years ago.
Everything you need to know
The Engine Itself
Apple’s Neural Engine is a custom-designed, low-power processor that is built specifically to handle tasks related to machine learning, such as image and speech recognition.
This engine is integrated into Apple’s latest products, including the latest Macbooks and iPhones. The Neural Engine is designed to be highly efficient and capable of handling a wide range of machine learning tasks, making it one of the most advanced and capable components of Apple’s hardware.
One of the key features of the Neural Engine is its ability to perform complex computations quickly and accurately. This is made possible by its unique architecture, which is optimized for machine learning algorithms. With the announcement of the A12 chip, the Neural Engine fitted inside was already capable of handling up to 5 trillion operations per second, making it one of the fastest machine learning processors on the market.
The Neural Engine is also designed to be energy-efficient, meaning that it can perform complex computations without consuming a lot of power. This is particularly important for mobile devices, such as the iPhone, where battery life is a critical factor. The Neural Engine is able to perform machine learning tasks while using very little power, which helps to extend the battery life of the device.
What It Can Do
One of the key applications of the Neural Engine is image recognition. The Neural Engine is able to analyze and categorize images quickly and accurately, making it possible to perform complex image-processing tasks in real time.
This is used in a number of Apple’s products, including the latest Macbooks, which use the Neural Engine to perform tasks such as object recognition, image stabilization, and even face recognition. As of right now, you can tap on any image on your iPhone, for example, and your device would immediately recognize any person or object in less than a second and with very high accuracy. A few years ago, no one would have thought that this could be done with a single tap and this quickly.
Another important application of the Neural Engine is speech recognition. The Neural Engine is able to perform real-time speech processing, making it possible to transcribe spoken words into text quickly and accurately. This is used in products such as Siri, Apple’s virtual assistant, which is able to recognize and respond to voice commands.
The Neural Engine is also used in other applications, such as natural language processing and computer vision. For example, the Neural Engine is able to analyze large amounts of text and understand the context and meaning of the words, which is used in applications such as Apple’s News app. The Neural Engine is also used in computer vision applications, such as object detection and tracking, which is used in applications such as Apple’s ARKit.
Another important feature of the Neural Engine is its ability to perform machine learning tasks in real time. This means that the Neural Engine is able to analyze and categorize data quickly, making it possible to perform complex machine-learning tasks in real time. This is particularly important for applications such as augmented reality, where real-time processing is critical.
The Neural Engine is also highly scalable, meaning that it can handle larger and more complex machine learning tasks as technology advances. This means that the Neural Engine will be able to support new and more advanced machine learning algorithms as they are developed, making it a key component of Apple’s future products.
Conclusions and personal thoughts
Apple is taking the Neural engine and its advancement pretty seriously, and this is absolutely the right move. Each year, their Neural engine is getting better and better, and with time, it will handle any task, no matter how big, with ease.
Once every other company decides to take AI more seriously, Apple will be already so far ahead, that there is no catching up. Apple’s line of current chips is already more powerful than ever and a lot better than the competitors in terms of pure power and efficiency, but now, even when it comes to AI and handling such tasks, Apple wanted to make sure that their chips are ahead in every single aspect, and I have to say, I think they are doing a great job so far!
In a few years, I want to see what Apple can achieve with such an engine, and if there is any room for improvement. I don’t mean making the engine itself a bit faster or capable of handling a large number of operations per second, but. I mean that I wish to see a new generation that is even more advanced and game-changing.
Supporting US
Check out our sponsors and affiliate links here: (We get a small cut every time you use these)
GET 50% off ExpressVPN Plus 3 Months Free by clicking here!
Skillshare: Get Your First Month for free here: https://skillshare.eqcm.net/XYWDXG & use code: ANNUAL30AFF for 30% OFF!!
Honey: To join honey, just click here!
PSST!! We are thinking of moving to SubStack really soon! If you are interested, check it out here! And while you are there, subscribe for more!
References