Quantitative modeling of racetrack memory, a tradeoff among area, performance, and power

Abstract

Recently, an emerging non-volatile memory called Racetrack Memory (RM) becomes promising to satisfy the requirement of increasing on-chip memory capacity. RM can achieve ultra-high storage density by integrating many bits in a tape-like racetrack, and also provide comparable read/write speed with SRAM. However, the lack of circuit-level modeling has limited the design exploration of RM, especially in the system-level. To overcome this limitation, we develop an RM circuit-level model, with careful study of device configurations and circuit layouts. This model introduces Macro Unit (MU) as the building block of RM, and analyzes the interaction of its attributes. Moreover, we integrate the model into NVsim to enable the automatic exploration of its huge design space. Our case study of RM cache demonstrates significant variance under different optimization targets, in respect of area, performance, and energy. In addition, we show that the cross-layer optimization is critical for adoption of RM as on-chip memory.