EDGE AI AND VLSI ARCHITECTURES FOR IOT

Edge AI has emerged as a key enabler for intelligent Internet of Things (IoT) systems by allowing data processing and decision-making to occur close to the data source. This chapter explores the role of VLSI architectures in supporting efficient Edge AI for IoT applications, where constraints on power, area, and latency are critical. It discusses specialized hardware architectures, including application-specific integrated circuits (ASICs), system-on-chip (SoC) designs, and hardware accelerators optimized for machine learning and deep learning workloads. Techniques such as parallel processing, approximate computing, and low-power design strategies are examined to achieve energy-efficient inference at the edge. The chapter also addresses design challenges related to scalability, security, and real-time operation in heterogeneous IoT environments. By highlighting the synergy between Edge AI algorithms and VLSI architectural innovations, this chapter provides insight into the development of intelligent, low-power, and high-performance IoT systems.