Embedded Systems
What is Embedded Systems?
Embedded systems are specialized computing systems designed to perform dedicated tasks within larger devices. In the context of Edge AI, these systems enable real-time processing and decision-making on connected devices, often without relying on cloud infrastructure. Also called integrated computing systems, they form the backbone of intelligent edge devices.
Why Is It Used?
Embedded systems are used to enhance device autonomy, reduce latency, and optimize data handling at the edge. By processing AI workloads locally, they improve performance, security, and reliability in applications like industrial automation, smart cameras, and IoT devices.
How Is It Used?
These systems are integrated into hardware such as sensors, gateways, and microcontrollers to execute AI algorithms directly on the device. This enables instantaneous analytics, anomaly detection, and predictive actions without sending large volumes of data to a central server.
Types of Embedded Systems
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Microcontrollers (MCUs): Low-power, task-specific chips.
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System-on-Chip (SoC): Combines CPU, GPU, and memory for AI workloads.
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FPGA-based systems: Flexible, high-performance devices for edge inference.
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ASICs: Custom-designed chips optimized for specific AI functions.
Benefits of Embedded Systems
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Low latency and real-time decision-making
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Reduced bandwidth and cloud dependency
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Enhanced security and data privacy
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Energy-efficient and cost-effective AI deployment