Loading your topics…
Shared by support using Learnlo
Create your own pack →Pick a topic to learn or start your exam journey.
0/15 topics mastered
Pick a topic to start learning!
A neural processing unit (NPU), also called an AI accelerator or deep learning processor, is specialized hardware (or a computer system) designed to speed up artificial intelligence and machine learning workloads such as artificial neural networks and computer vision. An NPU may be standalone or integrated as part of a CPU or GPU. The purpose of an NPU is to execute AI tasks more efficiently—either by running already trained models (inference) such as large language models, or by supporting model training. Compared with general-purpose compute, NPUs can deliver better speed and/or lower power consumption, and they are commonly used in robotics, Internet of Things (IoT), and sensor-driven, data-intensive applications. Architecturally, NPUs often use manycore or spatial designs and emphasize low-precision arithmetic and specialized dataflow or in-memory computing approaches to improve performance.
0/2 modes complete
An NPU (neural processing unit) is specialized hardware designed to accelerate artificial intelligence and machine learning workloads. Its role can differ depending on whether the system is running inference or training: for inference, the NPU efficiently executes already-trained AI models (including large language models), while for training it supports the computation needed to learn model parameters from data. In both cases, NPUs are typically optimized for speed and/or power efficiency, often using manycore or spatial architectures and low-precision arithmetic. On consumer and datacenter platforms, NPUs are commonly used to run models efficiently—especially inference—because they can execute low-bitwidth operations (e.g., INT4/INT8 and FP16/FP8) at high throughput (measured in TOPS). Training workloads are also supported by AI accelerators in general, including GPUs with dedicated low-precision matrix-multiplication hardware, but the content emphasizes inference as a primary, efficient use case for NPUs.
0/2 modes complete