Bus Network Integration for Enhanced SoC Performance and Power Consumption - Optimization of Tailored On-chip Interconnection for Low Power Digital System

The increasing demand for high-performance computing systems has prompted the devel- opment of advanced hardware optimization techniques to enhance the efficiency of data communication within integrated circuits. The primary objective of this research is to address the challenges associated with the design of efficient on-chip communication architectures. Traditional bus matrix designs often result in performance bottlenecks and excessive power consumption due to subop- timal routing strategies and limited bandwidth utilization. This work presents an investigation into the optimization of bus matrices to overcome above limitations leveraging hardware enhancements and to achieve significant improve- ments in both time and dynamic power consumption. The research begins with a com- prehensive review of the environment in which the thesis is developed by targeting the design choice for specific System on Chips. A critical analysis of the existing bus matrix designs highlights their limitations and the trade-offs between performance and power consumption, setting the stage for our proposed hardware-based optimization techniques. The custom hardware solution aims to achieve the following key objectives: 1. Improved Time Efficiency: The optimized bus matrix design minimizes data trans- fer latencies, enhancing overall system performance, by ensuring efficient data path selection, reducing contention and congestion. 2. Reduced Dynamic Power Consumption: The clock gating mechanisms prevents un- necessary state changes in the circuit, decreasing the dynamic power by associated with switching activities, while maintaining performance requirements. The research methodology involves pre and post synthesis simulation to evaluate the per- formance of the proposed bus matrix optimization techniques, proving the effectiveness of the hardware enhancements in achieving the desired improvements in time efficiency and dynamic power consumption. The proposed approach holds promise for the development of more energy-efficient and high-performance computing systems, which are essential for a wide range of applications, including data centers, embedded systems, and mobile devices.