A neural processing unit (NPU) is a specialized type of microprocessor designed to accelerate the performance of artificial neural networks (ANNs) and machine learning algorithms. These chips have been developed with the goal of accelerating the training and inference processes of deep learning models. NPUs can perform complex matrix calculations and parallel processing much more efficiently than traditional CPUs or GPUs. They are widely used in various applications such as computer vision, natural language processing, and speech recognition, among others.
The above paragraph provides a summarized brief on NPU. To gain more details on NPU, please read the rest of the article.
What are NPUs?
NPUs have become increasingly popular in recent years as the demand for AI and machine learning applications continues to grow. They enable these technologies by processing vast amounts of data with exceptional speed and accuracy. NPUs are capable of executing millions of operations per second, making them ideal for handling the complex calculations required in deep learning tasks compared to CPUs and GPUs. They are used in a variety of industries, including healthcare, finance, and transportation. For example, an NPU can be used in healthcare to analyze medical images and identify potential health issues. In the finance industry, an NPU can help with fraud detection and risk management. In transportation, an NPU can be used to process sensor data and improve self-driving car technology.
How NPUs Work?
One of the key operations that an NPU can perform is inference. We shall take an example of “inference” to understand how NPUs work. Inference is the process of using a trained neural network model to make predictions or decisions based on new input data. For example, let’s say you have a trained neural network that can identify objects in images. The neural network has learned to recognize different objects such as cars, trees, and people. When you provide a new image as input to the neural network, the NPU can perform inference to determine what objects are present in the image.
During inference, the NPU will take the input data, apply the learned weights and biases of the neural network to the data (essentially matrix operations), and output a prediction. The prediction could be a label indicating what object the neural network thinks is in the image or a probability score for each possible object class.
Advantages of NPUs
One of the significant advantages of an NPU is its ability to execute highly parallelized computations with low latency and high energy efficiency. This capability is achieved by using dedicated hardware to accelerate matrix operations, which are common in deep learning tasks. By offloading these calculations to the NPU, the CPU can focus on other tasks, improving overall system performance.
Industrial NPUs
There are several types of NPUs available in the market, including Google’s Tensor Processing Unit (TPU), Intel’s Neural Network Processor (NNP), Huawei’s Ascend NPU, etc. Each of these NPUs is designed for specific use cases, and their performance and capabilities vary.