A '''neural processor''' or a '''neural processing unit''' ('''NPU''') is a [[application-specific microprocessor|specialized]] circuit that implements all the necessary control and arithmetic logic necessary to execute [[machine learning]] algorithms, typically by operating on [[predictive models]] such as [[artificial neural network]]s (ANNs) or [[random forest]]s (RFs).

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. A 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.

A neural processing unit (NPU) is a well-partitioned circuit that comprises of all the control and arithmetic logic components necessary to execute [[machine learning]] algorithms. NPUs are designed to accelerate the performance of common machine learning tasks such as image classification, machine translation, object detection, and various other predictive models. NPUs may be part of a large SoC, a plurality of NPUs may be instantiated on a single chip, or they may be part of a dedicated neural-network accelerator.

* [[ARM]]: ML Processor

* [[NEC]]: SX-Aurora (VPU)