Low-Power VLSI Design for Image Analysis in Embedded Vision Systems

An innovative low-power Very Large Scale Integration (VLSI) architecture for real-time image identification in embedded vision systems is presented in this research study. The proposed design combines the cutting-edge hardware optimizations, algorithmic improvements, and power management practices in response to the rising demand for energy-efficient solutions across several applications, including robotics, surveillance, and autonomous cars. It starts with an effective sensor interface that uses power-saving analog-to-digital converters and smart clock gating techniques to reduce power usage while data is being acquired. Processing units specially created for high-performance operation with less power consumption include hardware accelerators for convolutional neural networks, specialized feature extraction units, and dedicated image identification accelerators. An adequately designed memory hierarchy improves energy efficiency, low-power interconnects, clock gating, and voltage scaling. The proposed architecture is completed with features for data parallelism, power management, and algorithmic improvements. The system's power utilization, real-time performance, and image identification precision have all undergone extensive testing using benchmark datasets and real-world scenarios. The proposed architecture is a solution for embedded vision systems because it strikes the perfect mix between real-time image identification and power economy.