What is NPU and how it Works ?

NPU stands for Neural Processing Unit, which is a type of specialized hardware accelerator designed for artificial intelligence (AI) and machine learning (ML) tasks. NPUs are typically used in devices such as smartphones, smart cameras, Internet of Things (IoT) devices, and other edge computing devices to perform AI/ML computations more efficiently and quickly.

NPUs are specifically designed to accelerate the inference phase of neural networks, which is the process of using a trained neural network to make predictions or decisions based on input data. The inference phase involves processing data through the trained neural network to generate output predictions, and NPUs are optimized for this task to provide high performance with low power consumption.

The working principle of an NPU involves several key components, including:

1. Data flow: NPUs use a data-driven approach, where data flows through the neural network in parallel, allowing for efficient processing of multiple data points simultaneously.
2. Parallel processing: NPUs utilize parallel processing techniques to perform multiple calculations concurrently, which speeds up the inference process.
3. Reduced precision: NPUs often use reduced-precision arithmetic, such as 16-bit or even lower, to accelerate computations while maintaining acceptable levels of accuracy.
4. Memory optimization: NPUs have specialized memory hierarchies and caching techniques to minimize data movement and optimize memory access, which helps to improve overall performance.
5. Hardware acceleration: NPUs leverage dedicated hardware components, such as matrix multiplication units and tensor cores, to accelerate key computations commonly used in neural networks.
6. Neural network optimizations: NPUs may implement various neural network optimizations, such as quantization, pruning, and quantized inference, to further enhance performance and efficiency.

Overall, NPUs are designed to accelerate the inference phase of neural networks by leveraging specialized hardware, optimized algorithms, and parallel processing techniques to achieve high performance and power efficiency for AI/ML workloads in edge devices.