Integrating Electronics and Communication Engineering in Advancing Artificial Intelligence: Hardware, Connectivity, and Innovation Synergies- A Review

Abstract

This paper explores the synergies between ECE and AI, highlighting how advancements in semiconductor technology, sensor systems, and communication networks have enabled the growth of AI applications. Electronics pro- vide the computational backbone for AI through specialized processors such as GPUs, TPUs, and neuromorphic chips, while sensors and IoT devices facilitate real-time data acqui- sition essential for AI training and inference. Concurrently, communication systems, including 5G and edge computing, ensure efficient data transmission and low-latency process- ing for AIdriven applications such as autonomous vehicles, smart cities, and healthcare monitoring. The integration of electronics and communication with AI also drives innovations in machine learning acceleration, energy-efficient computing, and realtime analytics. This paper concludes by discussing future opportunities, such as the role of quantum computing and next-generation communication protocols, in shaping the evolution of AI technologies. The findings underscore the critical importance of interdisciplinary approaches in fostering AI’s continued advancement.