Designing Neuro-Inspired Architectures for Efficient Signal Processing

Communication systems, picture and voice recognition, and data analysis are just a few examples of how signal processing has impacted other areas of contemporary technology. In this research, we compare a new strategy to various conventional ones for evaluating and designing neuro-inspired architectures for effective signal processing. To improve flexibility, energy economy, and real-time capabilities, the suggested technique combines neuro-adaptive learning with sparsity-based optimization and real-time event-driven processing. The suggested method was compared to the more popular Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Neural Network, and Naive Bayes to see which one worked better. Two full tables show the results of looking at key performance factors such as accuracy, speed, energy economy, flexibility, CPU usage, memory consumption, and power consumption. It was found that the suggested method is better than the most recent ones in terms of accuracy, speed, and saving resources. It was found to be more flexible and works in real time, which makes it a good choice for users that need to handle signals quickly and accurately. Besides that, the suggested method uses fewer resources, such as fewer CPU cycles, memory, and power, compared to normal methods. These results show that signal processing designs that are based on neurons can be useful. These designs have a lot of potential to solve a wide range of real-world problems.