Neural Engines
A Neural Engine is a specialized hardware component designed to accelerate machine learning (ML) and artificial intelligence (AI) computations. First introduced by Apple in the A11 Bionic processor in 2017, the Neural Engine is integrated into Apple Silicon chips and optimized for tasks such as image recognition, natural language processing, and real-time video analysis. It uses a matrix-based architecture to process large datasets efficiently, enabling applications like Face ID, Smart HDR, and advanced photo editing on devices like the iPhone and iPad. https://en.wikipedia.org/wiki/Neural_Processor
The role of a Neural Engine is to handle AI-specific tasks more efficiently than traditional CPU or GPU components. For instance, the Neural Engine in the A14 Bionic can perform up to 11 trillion operations per second, while those in the M1 and M4 series deliver even higher performance. This hardware acceleration allows developers to build more complex machine learning models while ensuring that tasks are processed faster and with lower power consumption, critical for mobile and embedded devices. https://www.apple.com/newsroom/2020/11/introducing-m1-apple-silicons-first-system-on-a-chip-for-mac
Beyond Apple, other companies like Google and NVIDIA have developed their own versions of neural accelerators, such as TPUs (Tensor Processing Units) and Tensor Cores. These technologies enable efficient on-device AI processing for applications in data centers, autonomous vehicles, and IoT devices. By offloading AI workloads from general-purpose processors to dedicated Neural Engines, modern devices can achieve better performance and energy efficiency while scaling the capabilities of AI across diverse platforms. https://cloud.google.com/tpu