Design of an Area-Efficient Computing in Memory Platform Based on STT-MRAM
Chao Wang, Zhaohao Wang, Gefei Wang, Youguang Zhang, Wang Kang
Abstract
In the era of big data, the memory wall between the processor and the memory as well as leakage current have become major bottlenecks of the traditional CMOS-based Von-Neumann computer architecture. Computing-in-memory (CiM) based on non-volatile memories (NVMs) is considered a promising method to solve the above-mentioned issues in computing systems. In this article, we propose a CiM platform based on spin-transfer torque magnetic random access memory (STT-MRAM). On the basis of the conventional CiM with AND/OR logic functions, the full-adder (FA) arithmetic operation is achieved with slight circuits modification by exploiting majority logic. Due to the parallel processing of carry and sum operations in multi-bit FA operation, the latency overhead caused by the two-step scheme is acceptable. The hybrid spintronic/CMOS simulations on the 40 nm technology node prove the functionality and performance of the proposed CiM platform.