Field Programmable Gate Array (FPGA)
What is Field Programmable Gate Array?
A Field Programmable Gate Array (FPGA) is a reconfigurable semiconductor device that allows developers to customize hardware functions after manufacturing. In Edge AI, FPGAs enable low-latency, high-efficiency AI inference directly on devices—making them ideal for processing data closer to the source.
Why Is It Used?
FPGAs are used in Edge AI systems to handle real-time data processing with minimal power consumption. Their adaptability allows engineers to fine-tune hardware logic for specific machine learning tasks, ensuring faster execution and reduced dependency on centralized cloud servers.
How Is It Used?
In Edge AI deployments, FPGAs accelerate neural network inference, sensor fusion, and video analytics. By offloading compute-intensive workloads from CPUs or GPUs, they deliver parallel processing performance that supports autonomous machines, IoT sensors, and smart surveillance at the edge.
Types of Field Programmable Gate Array
Static FPGAs – Configured once for fixed logic functions.
Dynamic/Partial Reconfiguration FPGAs – Allow specific regions to be reprogrammed without halting operations.
SoC FPGAs – Combine CPU cores and FPGA fabric for hybrid processing.
Benefits of Field Programmable Gate Array
Ultra-low latency for AI inference at the edge
Energy-efficient alternative to GPUs
Hardware flexibility for evolving AI models
Scalable performance across Edge IoT devices
Improved data privacy by keeping computation local