DESIGN AND ANALYSIS OF A HIGH-SPEED MEMORY CONTROLLER FOR MEMORYCENTRIC COMPUTING APPLICATIONS

Memory-centric computing (MCC) has gained significant attention in recent years due to the increasing demand for data-intensive applications in various domains such as big data analytics, machine learning, and scientific simulations. MCC aims to improve the performance and energy efficiency of these applications by bringing computation closer to memory and minimizing data movement between the CPU and memory. In this article, we present the design and analysis of a high-speed memory controller for MCC applications. The proposed memory controller is based on a hybrid memory architecture that combines high-bandwidth memory (HBM) and dynamic random-access memory (DRAM) to provide a scalable and efficient memory system for MCC workloads. We also propose a novel memory access scheduling algorithm that optimizes the utilization of the available memory bandwidth and minimizes the memory access latency. We evaluate the performance of the proposed memory controller using a set of benchmark applications and compare it with state-of-the-art memory controllers. Our results demonstrate that the proposed memory controller can achieve significant performance improvements over existing solutions, particularly for memorybound MCC workloads. The proposed memory controller can be integrated into existing systems to enable efficient and scalable memory-centric computing, which has the potential to accelerate a wide range of data-intensive applications.