LIMITA: Logic-in-Memory Primitives for Imprecise Tolerant Applications.

Magnetic tunnel junction (MTJ) with non-volatility, and near-zero leakage power features are believed to be the most prominent candidates for CMOS substitution. Logic-in-memory reduces the memory wall challenge and takes advantage of these features, but still has the challenge of writing to a magnetic device with regard to power consumption and device failure. Considering these challenges, we propose new designs in the approximate computing approach for magnetic device-based primitives. This paper proposes several power- and/or delay-efficient configurable Logic-in-Memory for Imprecise Tolerant Applications (LIMITA) based on spin transfer torque (STT) and STT-assisted voltage control magnetic anisotropy (VCMA) magnetic devices. Image processing is one of the most widely used imprecise tolerant applications with a high computational volume. Therefore, we map LIMITA with several approximation boundaries in Sobel, Sharpening, and Smoothing algorithms. To evaluate LIMITA at circuit and application levels, we did extensive simulations in HSPICE and MATLAB, respectively. Compared to the state-of-the-art works, the exact design of LIMITA improves the number of transistors in the logic tree up to 66% with 12% power overhead due to its configurability feature. In addition, in comparison with approximate primitives, LIMITA- $\text{F}_{\mathrm {i}}$ improves delay, power consumption, and the number of transistors by $4.5\times$ , $71.5\times$ , and $2\times$ respectively. [ABSTRACT FROM AUTHOR]