Design space exploration of magnetic tunnel junction based stochastic computing in deep learning

Magnetic tunnel junction (MTJ) is considered as a promising memory candidate in the more than Moore era because of high power efficiency, fast access speed, nearly infinite endurance and easy 3D integration. The nondeterministic switching behavior has been profited to exploit new directions for computing methods, such as stochastic computing. In this paper, the application of stochastic switching behavior in stochastic computing is explored for deep neural network (DNN). Stochastic computing method features low logic complexity, low energy consumption and fine-grained parallelism, boosting the performance of DNN system by combining MTJ. As a key block of stochastic computing, MTJ based true random number generator design is presented in details. The functionality has been validated by combining the hardware design and post-processing in software. Simulation results are demonstrated visibly by handwritten digits recognition test to show the accuracy. Furthermore, the performance is investigated in terms of accuracy, energy consumption and memory occupation to find more efficient techniques.