DRAM density scaling has become increasingly difficult due to challenges in maintaining a sufficiently high storage capacitance and a sufficiently low leakage current at nanoscale feature sizes. Non-volatile memories (NVMs) have drawn significant attention as potential DRAM replacements because they represent information using resistance rather than electrical charge. Spin-torque transfer magnetoresistive RAM (STT-MRAM) is one of the most promising NVM technologies due to its relatively low write energy, high speed, and high endurance. However, STT-MRAM suffers from its own scaling problems. As the size of the storage element decreases with technology scaling, STT-MRAM retention error rates are expected to increase, which will require multi-bit error-correcting code (ECC) and periodic scrubbing. We introduce the Sanitizer architecture, which mitigates the performance and energy overheads of ECC and scrubbing in future STT-MRAM based main memories. To reduce the scrubbing rate, a coarse-grained, multi-bit ECC mechanism with a 12.5 percent storage overhead is used. To avoid fetching multiple blocks from memory and performing costly ECC checks on every read, the memory regions that will likely be accessed in the near future are predicted and proactively scrubbed. Compared to a conventional STT-MRAM system, Sanitizer improves performance by 1.22× and reduces end-to-end system energy by 22 percent.

A field-assisted spin-torque transfer magnetoresistive RAM (STT-MRAM) cache is presented for the use in high-performance energy-efficient microprocessors. Adding field assistance reduces the switching latency by a factor of 4. An array model is developed to evaluate the switching energy for different field currents and array sizes. Several STT-MRAM-based cells demonstrate a 55% energy reduction as compared with an SRAM cache subsystem. As compared with STT-MRAM caches with subbank buffering and differential writes, a field-assisted STT-MRAM cache improves the system performance by 28%, with a 6.7% increase in energy.

With technology scaling, on-chip power dissipation and off-chip memory bandwidth have become significant performance bottlenecks in virtually all computer systems, from mobile devices to supercomputers. An effective way of improving performance in the face of bandwidth and power limitations is to rely on associative memory systems. Recent work on a PCM-based, associative TCAM accelerator shows that associative search capability can reduce both off-chip bandwidth demand and overall system energy. Unfortunately, previously proposed resistive TCAM accelerators have limited flexibility: only a restricted (albeit important) class of applications can benefit from a TCAM accelerator, and the implementation is confined to resistive memory technologies with a high dynamic range (RHigh/RLow), such as PCM.

Power dissipation and off-chip bandwidth restrictions are critical challenges that limit microprocessor performance. Ternary content addressable memories (TCAM) hold the potential to address both problems in the context of a wide range of data-intensive workloads that benefit from associative search capability. Power dissipation is reduced by eliminating instruction processing and data movement overheads present in a purely RAM based system. Bandwidth demand is lowered by processing data directly on the TCAM chip, thereby decreasing off-chip traffic. Unfortunately, CMOS-based TCAM implementations are severely power- and area-limited, which restricts the capacity of commercial products to a few megabytes, and confines their use to niche networking applications.