Exploring STT-MRAM Based In-Memory Computing Paradigm with Application of Image Edge Extraction

In this paper, we propose a novel Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM) array design that could simultaneously work as non-volatile memory and implement a reconfigure in-memory logic operation without add-on logic circuits to the memory chip. The computed output could be simply read out like a typical MRAM bit-cell through the modified peripheral circuit. Such intrinsic in-memory computation can be used to process data locally and transfers the "cooked" data to the primary processing unit (i.e. CPU or GPU) for complex computation with high precision requirement. It greatly reduces power-hungry and long distance data communication, and further leads to extreme parallelism within memory. In this work, we further propose an in-memory edge extraction algorithm as a case study to demonstrate the efficiency of in-memory preprocessing methodology. The simulation results show that our edge extraction method reduces data communication as much as 8x for grayscale image, thus greatly reducing system energy consumption. Meanwhile, the F-measure result shows only ∼10% degradation compared to conventional edge detection operators, such as Prewitt, Sobel and Roberts.