Graphics Processing Unit (GPU)
What is Graphics Processing Unit?
A Graphics Processing Unit (GPU) is a specialized processor designed to handle complex mathematical computations, primarily for rendering graphics and accelerating AI workloads. In Edge AI, GPUs enable real-time data processing directly on edge devices, reducing latency and dependence on cloud infrastructure.
Why Is It Used?
GPUs are used in Edge AI to execute high-performance parallel computations required for machine learning, image recognition, and neural network inference. They empower edge devices to make faster, autonomous decisions—crucial for real-time applications like predictive maintenance, smart surveillance, and autonomous vehicles.
How Is It Used?
In Edge Computing, GPUs process data locally by accelerating AI models and inference tasks on devices like edge servers, gateways, or embedded systems. They work alongside CPUs to offload intensive workloads, optimizing system efficiency and reducing bandwidth usage.
Types of Graphics Processing Unit
Integrated GPUs: Built into CPUs, suitable for lightweight AI tasks at the edge.
Discrete GPUs: Standalone units with dedicated memory, ideal for demanding Edge AI workloads.
Embedded GPUs: Optimized for compact, low-power devices used in IoT and industrial applications.
Benefits of Graphics Processing Unit
Real-time processing: Enables instant AI-driven insights at the edge.
Reduced latency: Minimizes cloud dependence and network delays.
Energy efficiency: Handles complex computations with optimized power use.
Scalability: Supports multiple concurrent AI models on a single device.