In-memory computing based on phase change memory for high energy efficiency

The energy efficiency issue caused by the memory wall in traditional von Neumann architecture is difficult to reconcile. In-memory computing(CIM) based on emerging nonvolatile memory(NVM) is a promising solution to avoid data movement between storage and processors and realize highly energy-efficient computing. Compared with other NVM technologies, phase change random access memory(PCM) exhibits comprehensive performance for analog computing. In this paper, we review advanced PCM techniques,including phase-change materials, mechanisms, and unique properties, as a foundation and inspiration for implementing CIM architecture. Meanwhile, state-of-the-art PCM-based CIM systems are well discussed for high energy efficiency in artificial neural networks, spiking neural networks, and other artificial intelligence(AI) applications. Finally, we present the remaining challenges and potential solutions of CIM for further investigation.