Knowledge Center
Knowledge Center

Neural Networks

A method of collecting data from the physical world that mimics the human brain.


Neural networking itself is in a state of almost constant flux and development, which makes it something of a moving target. There are more than 20 different types of neural networks today. Some are more in favor one month than the next.

On top of that, some approaches are considered better than others for certain tasks. For example, convolutional neural networks (CNNs), which have emerged as the centerpiece of embedded vision in cars and drones, may be replaced or supplemented by recurrent neural networks (RNNs). An RNN can help distinguish not just whether an object is a dog or a person, but it can determine what it is doing over time. A dog may be moving into the road, or it may be moving away from the road. Or a child may be chasing after a ball that is heading into a busy road. But a CNN will only give snapshots of the movement, while an RNN will provide enough data over a period of time to determine whether a car needs to brake and how quickly.

Each of these approaches has tradeoffs. An RNN provides much better context for understanding moving image data, but it means processing an extra dimension of data. How much of that data gets processed locally versus centrally can affect how quickly a vehicle can react, how much power it consumes, how long the sensor network will be reliable, and the overall architecture of a system. And just to add more confusion, algorithms are still being developed to utilize this data more efficiently, which can affect any or all of these factors.