TAM: A Computing in Memory based on Tandem Array within STT-MRAM for Energy-Efficient Analog MAC Operation

Abstract

Computing in memory (CIM) has been demonstrated promising for energy efficient computing. However, the dramatic growth of the data scale in neural network processors has aroused a demand for CIM architecture of higher bit density, for which the spin transfer torque magnetic RAM (STT-MRAM) with high bit density and performance arises as an up-and-coming candidate solution. In this work, we propose an analog CIM scheme based on tandem array within STT-MRAM (TAM) to further improve energy efficiency while achieving high bit density. First, the resistance summation based analog MAC operation minimizes the effect of low tunnel magnetoresistance (TMR) by the serial magnetic tunnel junctions (MTJs) structure in the proposed tandem array with smaller area overhead. Moreover, a read scheme of resistive-to-binary is designed to achieve the MAC results accurately and reliably. Besides, the data-dependent error caused by MTJs in series has been eliminated with a proposed dynamic selection circuit. Simulation results of a 2Kb TAM architecture show 113.2 TOPS/W and 63.7 TOPS/W for 4-bit and 8-bit input/weight precision, respectively, and reduction by 39.3% for bit-cell area compared with existing array of MTJs in series.