A Framework for SOT-MRAM Scaling Road-Map with Density and Application Evaluation
H D Kallinatha, Basavaraj Talawar · 2024
The increasing difference between CPU speeds and memory access times, known as the “Memory Wall” problem, poses considerable challenges in modern computing. This study introduces a scaling factor framework to integrate Spin-Orbit-Torque Magnetic RAM(SOT-MRAM) into cache architectures as a potential replacement for Static Random Access Memory(SRAM). This research primarily targets applications in artificial intelligence (AI), natural language processing(NLP), and broad computing tasks. It presents a method to evaluate the effectiveness of scaling factor framework and density enhancement in cache memory through the proposed frame-work's extensive Design Space Exploration(DSE). This exploration includes a detailed comparative analysis of SRAM and SOT-MRAM under various scaling conditions within the L2 and Last-Level Cache(LLC) segments. The outcomes indicate that SOT-MRAM significantly improves energy efficiency and reduces latency, achieving a 60% decrease in power usage and a 75% enhancement in response times compared to conventional SRAM caches. These advancements suggest that SOT-MRAM could effectively mitigate the challenges the Memory Wall poses, enhancing overall computational performance.