Executing [[deep neural networks]] such as [[convolutional neural networks]] means performing a very large amount of [[multiply-accumulate operations]], typically in the billions and trillions of iterations. The large number of iterations comes from the fact that for each given input (e.g., image), a single convolution comprises of iterating over every channel and then every pixel and performing a very large number of MAC operations. And many such convolutions are found in a single model and the model itself must be executed on each new input (e.g., every camera frame capture).

Unlike traditional [[central processing units]] which are great at processing highly serialized instruction streams, Machine learning workloads tend to be highly parallelizable, much like a [[graphics processing unit]]. Moreover, unlike a GPU, NPUs can benefit from vastly simpler logic because their workloads tend to exhibit high regularity in the computational patterns of [[deep neural networks]]. For those reasons, many custom-designed dedicated neural processors have been developed.