For ultimate parallel computing systems, to process what is computed at the synapse in neuron, preference is given for the implementation of the devices with the same size as the BL of the synapse array. Utilizing the capability to provide instantaneous current by the selector-based compact neurons enabled effective classification of the analog weighted sum current based on integrate-and-fire or oscillation frequency modulation technique. By precisely fine-tuning the ion migration and phase transition to have multiple states of the nonvolatile PCM and RRAM for the analog synapse, and intentionally enhancing the volatility of the memory for the neuron, all emerging memory-based neuromorphic systems have been reported.
Several aspects of the implementation and utilization of the neuromorphic hardware have remained unexplored. Hence, important features of the synaptic and neuronal devices may differ from speed, energy, and capacity perspectives depending on the applications ranging from cloud, fog, and edge computing. In particular, unlike the conventional silicon transistors, in which performances have been improved primarily by geometrical scaling and cell design, the synaptic and reliability characteristics of each emerging device are strongly related to the materials used. Further, we believe that unconventional computing platforms are not limited to emerging device technologies, and it can be realized by CMOS and new devices integrated systems.[126] Mixed CMOS-emerging memories hardware can make cognitive tasks more efficient, and will be an intermediate step before ultimately implementing future computing systems implemented entirely with non-CMOS devices. Therefore, it is hoped that the findings and approaches discussed in this article will be a stepping stone toward significant technological advances that can lead to social change beyond building neuromorphic hardware systems.

Acknowledgements

This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT) (NRF-2020M3F3A2A01081775 and NRF-2021R1C1C1003261).

Conflict of interest

The authors declare no conflict of interest.

References