Edge AI Hardware is transforming industries by bringing artificial intelligence (AI) processing closer to the data source. By performing AI computations locally on devices rather than in the cloud, Edge AI Hardware reduces latency, enhances privacy, and improves efficiency in real-time applications.

What is Edge AI Hardware?
Edge AI Hardware refers to specialized processors, accelerators, and embedded chips that execute AI models directly on edge devices, such as smartphones, IoT devices, robots, and autonomous vehicles. These chips enable real-time data processing and decision-making without relying on cloud computing.

Key Components of Edge AI Hardware
1. AI Processors (NPUs, TPUs, GPUs, FPGAs, ASICs)
Neural Processing Units (NPUs) – Optimized for deep learning tasks.

Tensor Processing Units (TPUs) – Designed for AI inference and training.

Graphics Processing Units (GPUs) – Accelerate AI workloads, commonly used in computer vision and deep learning.

Field Programmable Gate Arrays (FPGAs) – Offer customizable AI acceleration for real-time applications.

Application-Specific Integrated Circuits (ASICs) – Tailored for power-efficient AI processing.

2. Edge AI Modules & Boards
NVIDIA Jetson Series – Popular for AI-driven robotics and autonomous systems.

Google Coral – Offers efficient AI inference for IoT applications.

Intel Movidius & OpenVINO – Used in smart cameras and industrial automation.

3. AI-Enabled Microcontrollers & SOCs (System-on-Chip)
Embedded AI microcontrollers for low-power AI processing.

Found in smart home devices, wearable tech, and IoT sensors.