Design Considerations and Benefits of Storage Class Memory (SCM) for Data‑Intensive Applications

Facing the growing memory demands of emerging data services, manufacturers of Storage Class Memory (SCM) are entering the market to provide low‑latency, low‑cost alternatives to traditional DRAM. SCM technologies include Intel Optane, MRAM, ReRAM, FRAM, Fast NAND, and carbon‑based NRAM, each with distinct performance, capacity, and cost characteristics.

By 2022 the SCM market is projected to reach about $2.7 billion, a fraction of the $100 billion memory industry, yet several PCM and Fast NAND solutions have already been deployed, offering latency improvements despite lower endurance compared with DRAM.

SCM can be positioned between SRAM and DRAM, delivering sub‑memory latency; MRAM, for example, provides fast access with small capacity and higher cost, while PCM offers larger capacity with higher latency. These trade‑offs affect suitability for different workloads.

Three key benefits of SCM are illustrated:

Accelerating emerging applications: AI training workloads that process terabytes of data can benefit from SCM’s lower latency, reducing the time spent on I/O and simplifying programming models.

Fast data recovery (“store‑as‑you‑use”): Persistent SCM enables instant database or service restart without the traditional IO‑bound warm‑up, meeting strict recovery‑time objectives.

Green data‑center operation: Persistent SCM reduces DRAM refresh power, lowering overall data‑center energy consumption.

However, SCM faces challenges such as “awkward latency” where access times (e.g., 300 ns for PCM) can stall CPU cores, forcing difficult scheduling decisions, and the need for sophisticated memory‑access modeling to place hot versus cold data on appropriate media.

Effective SCM deployment requires coordinated design across CPU, operating system, and applications, leveraging insights from SSD adoption (hot/cold data segregation) and developing new algorithms beyond simple LRU caching.

In summary, SCM addresses performance, programming simplicity, persistence, and energy efficiency pressures of modern data workloads, but its early‑stage ecosystem demands careful integration and continued research to realize its full potential.