Design & Verification of AI Enabled Reconfigurable SRAM Controller for Automobile Application

Abstract

The rapid advancements in artificial intelligence (AI) and automotive technology have necessitated the development of sophisticated and intelligent memory management systems. This research focuses on the design and validation of an AI-enabled reconfigurable Static Random-Access Memory (SRAM) controller tailored for automotive applications. By leveraging AI-based algorithms, the proposed controller enhances the efficiency of memory allocation, reduces access time, and optimizes power consumption, thereby improving the overall performance and reliability of automotive electronic systems. The controller's reconfigurable architecture allows for dynamic adaptation to varying workloads, ensuring efficient data handling in real-time applications such as in-vehicle entertainment systems and Advanced Driver Assistance Systems (ADAS).

To ensure operational accuracy and optimal performance, the design is coded in Hardware Description Language (HDL) and verified through simulation and FPGA-based prototyping. The verification process employs industry-standard methods, such as Universal Verification Methodology (UVM), to assess the SRAM controller's reliability and resilience. Test results indicate significant improvements in speed, power efficiency, and flexibility compared to conventional SRAM controllers, positioning it as a suitable candidate for automotive systems of the future