Review Article


2021, 14(9): 3126–3142


Memory-centric neuromorphic computing for unstructured data processing

Sang Hyun Sung, Tae Jin Kim, Hera Shin, Hoon Namkung, Tae Hong Im, Hee Seung Wang, and Keon Jae Lee (✉)

View Author's information

Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

Keywords: neuromorphic computing, memory-centric, memristor, artificial synapses, artificial neurons, memristive neural network
Full article PDF
Cite this article(Endnote)
Share this article

views: 234

Citations: 0

  • Abstract
  • References
The unstructured data such as visual information, natural language, and human behaviors opens up a wide array of opportunities in the field of artificial intelligence (AI). The memory-centric neuromorphic computing (MNC) has been proposed for the efficient processing of unstructured data, bypassing the von Neumann bottleneck of current computing architecture. The development of MNC would provide massively parallel processing of unstructured data, realizing the cognitive AI in edge and wearable systems. In this review, recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories, volatile switches, synaptic plasticity, neuronal models, and memristive neural network.
Related Article
Cite this article

Memory-centric neuromorphic computing for unstructured data processing. Nano Res. 2021, 14(9): 3126–3142

Download citation