AI-Driven Fault Detection and Power Optimization in VLSI Circuits Using Machine Learning Techniques

The proposed FPGA-based architecture was conceptually evaluated against conventional CPU and GPU platforms across three key metrics: power consumption, inference throughput, and energy efficiency. FPGAs demonstrate significantly lower power consumption compared to GPUs while maintaining competitive inference throughput suitable for real-time edge applications. The architecture achieves superior performance-per-watt, making it highly suitable for resource-constrained embedded environments such as IoT devices, autonomous systems, and smart healthcare applications. Dynamic voltage-frequency scaling further reduces power consumption during low-workload periods, while quantization and pruning techniques maintain model accuracy within acceptable bounds for edge deployment.

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