MTJ-based random number generation and its application in SNN handwritten digits recognition

Abstract

Spiking Neural Networks (SNNs) that require synapse weight initialization using random numbers have been widely used in the neural morphological system. However, the random numbers generated by traditional digital circuits have certain repeatability, and the entire computing architecture has issues such as high resource consumption and low integration. In this letter, a hardware system for true random number generation is realized through integrating a magnetic tunnel junction, a memory cell of MRAM (magnetic random access memory) chips, with an interface circuit and using the same mechanism as writing data in spin transfer torque MRAM. The generated true random numbers are evaluated using NIST SP800-22 standard and are used for synapse weight initialization in an SNN system. The recognition rate of the system initialized by the generated true random numbers is about 84% for an MNIST handwritten digit dataset, which is 2%–3% higher than that using a traditional linear feedback shift register. The reported work provides a new approach for better SNN performance.