Approximate SOT-MRAM for Neural Network Acceleration with Superior Read Performance

Magnetoresistive random-access memory (MRAM) has been demonstrated to be a suitable memory technology for neural network (NN) acceleration due to its non-volatility, high density, and fast access speed. However, compared to the widely used static random-access memory (SRAM), MRAM still exhibits a notable disparity in speed and energy. In this paper, we propose a read-related approximation computation (RAC) strategy based on spin-orbit torque MRAM (SOT-MRAM) to enhance the computational speed of NN, and then we introduce a reference reconfigurable array (RRA) architecture to further decrease the read latency and energy consumption, significantly improving the speed and energy efficiency of weight retrieval during computations. Furthermore, we propose an algorithm to verify and optimize NN model performance. The proposed architecture is evaluated using a 28 nm process combined with a SPICE model of the SOT-MRAM. Simulation results indicate that the read speed increases by 4.71X, the read energy consumption is reduced by 70.7%, while the model accuracy loss remains below 1%.