Artificial neural network

An artificial neural network (ANN) is a network of many very simple processors ("units" or "neurons"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data to make machine learning possible. The units operate only on their local data and on the inputs they receive via the connections.

Description
A neural network is a processing device, either an algorithm or dedicated hardware, whose design was inspired by the design and functioning of animal brains and components thereof to simulate artificial intelligence.

Most neural networks have some sort of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, neural networks "learn" from examples, just like children learn to recognize dogs from examples of dogs, and exhibit some structural capability for generalization.

Neurons are often elementary non-linear signal processors (in the limit they are simple threshold discriminators). Another feature of NNs which distinguishes them from other computing devices is a high degree of interconnection which allows a high degree of parallelism. Further, there is no idle memory containing data and programs, but rather each neuron is pre-programmed and continuously active.

The term "neural net" should logically, but in common usage never does, also include biological neural networks, whose elementary structures are far more complicated than the mathematical models used for ANNs.

Usage by Apple
Apple introduced the Neural Engine in September 2017 as part of the Apple A11 "Bionic" chip to enable Face ID detection in devices like the iPhone X. Apple has increased the number of transisters dedicated to the Neural Engine in its subsequent processors while also reducing power usage for neural operations.