Edge AI

Edge AI focuses on performing artificial intelligence computations directly on resource-constrained devices at the network's edge, minimizing latency and bandwidth needs while enhancing privacy. Current research emphasizes developing energy-efficient model architectures (like lightweight CNNs and Transformers), efficient model compression techniques (pruning, quantization), and hardware acceleration (using TPUs, NPUs, FPGAs, and specialized ASICs) to enable real-time inference on edge devices. This field is crucial for applications ranging from autonomous systems and industrial monitoring to healthcare and smart homes, driving advancements in both hardware and software for efficient and privacy-preserving AI deployment.