A Dual-Mode In-Memory Computing Unit Using Spin Hall-Assisted MRAM for Data-Intensive Applications

Abstract

The emerging big data application poses many non-traditional challenges to a conventional computing system. The processing of a large amount of data in a conventional computing system involves frequent data movement between the processor and the memory. As a result, power consumption and latency of the system increases, most commonly known as the von Neumann bottleneck. To address this von Neumann bottleneck, processing in memory is considered one of the most promising solutions for energy-efficient computing in data-intensive applications. Therefore, in this work, we propose an in-memory computing unit based on emerging non-volatile magnetic random access memory (MRAM), which can perform computations directly within the memory. We call the proposed memory unit computational MRAM (C-MRAM). The proposed array works in two modes: 1) memory mode, where it acts as standard memory with the additional advantage of non-volatility, and 2) logic mode, where it performs the computation inside the memory array. To demonstrate the benefits of the proposed C-MRAM array, we realize an approximate adder (Ax-ADD) for energy-constraint applications in which further power saving is achieved by relaxing the demand for computational accuracy. Furthermore, we propose to use the C-MRAM array in hybrid multi-bank architecture, which offers the flexibility of reconfiguring the proposed C-MRAM as a memory array or logic array to meet the requirement of a wide range of applications. The simulation result shows that the proposed array can efficiently perform memory, logic, and arithmetic operation in 2, 4, and 6 ns while consuming 0.2, 0.47, and 0.7 pJ of energy.