Sky-NN: Enabling Efficient Neural Network Data Processing with Skyrmion Racetrack Memory

The thriving of artificial intelligence has brought numerous efforts to build strengthened and sophisticated neural network models to resolve almost all kinds of problems in different academic fields. Owing to the growing complexity and size of neural networks, nonvolatile random access memory (NVRAM) has been utilized to avoid excessive data movements between volatile memory and persistent storage. Among various NVRAM alternatives, skyrmion racetrack memory (SK-RM) is regarded as a promising candidate owing to its high memory density and efficient reads and writes. Nevertheless, due to the distinct shift operation of SK-RM, directly applying existing data process methods of neural networks on SK-RM hinders the benefits and performance of both SK-RM and neural networks. To resolve this issue, this paper proposes Sky-NN to enable efficient NN data processing methods on SK-RM by utilizing the distinct shift and re-assemblability capability of skyrmions. A series of experiments were conducted to demonstrate the capability of Sky-NN.