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Area of Science:

  • Neuromorphic Engineering
  • Solid-State Electronics
  • Artificial Intelligence Hardware

Background:

  • The exponential growth of data necessitates advanced computing solutions to overcome power consumption challenges.
  • Neuromorphic electronics, inspired by biological systems, offer in-memory computing to bypass the von Neumann bottleneck.
  • NAND flash memory presents a competitive, non-volatile technology for large-scale data storage and processing.

Purpose of the Study:

  • To provide a comprehensive overview of recent advancements in neuromorphic computing utilizing NAND flash memory.
  • To explore various neuromorphic architectures, including off-chip and on-chip learning paradigms, based on NAND flash technology.
  • To discuss the fundamental aspects of NAND flash memory relevant to its application in neural network structures.

Main Methods:

  • Review of existing literature on neuromorphic computing architectures employing NAND flash memory.
  • Analysis of off-chip learning architectures with varying input and weight quantization levels.
  • Examination of on-chip learning architectures using backpropagation and feedback alignment algorithms.

Main Results:

  • Detailed discussion of NAND flash memory's array architecture, operational schemes, and electrical characteristics for neuromorphic applications.
  • Comparison of array architecture discrepancies between on-chip and off-chip learning implementations.
  • Demonstration of NAND flash memory's potential in diverse neural network structures.

Conclusions:

  • NAND flash memory-based neuromorphic computing provides a viable pathway to reduced power consumption in data-intensive tasks.
  • This review establishes a foundational understanding for utilizing NAND flash memory in neuromorphic computing systems.
  • The findings offer guidance for leveraging NAND flash memory according to specific application requirements.