Core Viewpoint - The article argues for a paradigm shift from traditional memory hierarchies to specialized memory architectures that leverage application-specific access patterns, proposing two new memory categories: Long-term RAM (LtRAM) and Short-term RAM (StRAM) [2][4][45]. Group 1: Current Memory Landscape - SRAM and DRAM have reached fundamental physical limitations, halting their scalable development, which has made memory a major bottleneck in performance, power consumption, and cost for modern computing systems [4][10]. - DRAM accounts for over 50% of server hardware costs, highlighting the economic impact of memory limitations [4][10]. - The rise of memory-intensive workloads, particularly in artificial intelligence, exacerbates the challenges posed by the stagnation of SRAM and DRAM [4][10]. Group 2: Proposed Memory Categories - LtRAM is designed for persistent, read-intensive data with long lifecycles, while StRAM is optimized for transient data that is frequently accessed and has short lifecycles [12][26]. - These categories allow for tailored performance optimizations based on specific workload requirements, addressing the mismatch between current memory technologies and application needs [12][26]. Group 3: Emerging Memory Technologies - New memory technologies such as RRAM, MRAM, and FeRAM offer different trade-offs in density, durability, and energy consumption, making them suitable for various applications but not direct replacements for SRAM or DRAM [16][21]. - RRAM can achieve density up to 10 times that of advanced HBM4 configurations, indicating significant scalability advantages [20][21]. Group 4: Workload Analysis and Memory Access Patterns - Analyzing memory access patterns is crucial for identifying opportunities for specialization, as seen in workloads like large language model inference, which is read-intensive and requires high bandwidth [28][30]. - Server applications and machine learning workloads exhibit diverse memory access patterns that can benefit from specialized memory technologies [29][31]. Group 5: System Design Challenges - The introduction of LtRAM and StRAM presents new research challenges, including how to expose memory characteristics to software without increasing complexity [35][37]. - Data placement strategies must adapt to heterogeneous memory systems, requiring fine-grained analysis of data lifecycles and access patterns [38][39]. Group 6: Power Consumption and Efficiency - Memory specialization can lead to significant power savings by aligning storage unit characteristics with workload demands, thus reducing static power and data movement costs [41][43]. - The increasing power density in data centers necessitates innovative cooling solutions and power management strategies to support high-performance computing [43][44].