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Alessandro Milozzi1, Saverio Ricci1, Daniele Ielmini2

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Area of Science:

  • Neuroscience
  • Materials Science
  • Computer Engineering

Background:

  • Biological neural networks offer superior energy efficiency and computing capabilities.
  • Resistive switching memory (RRAM) devices are crucial for scalable, energy-efficient neuromorphic computing.
  • Implementing spatiotemporal processing primitives with RRAM circuits is an ongoing challenge.

Purpose of the Study:

  • To develop neuromorphic circuits inspired by the human auditory system for memristive tonotopic mapping.
  • To demonstrate signal processing features of the cochlea using volatile RRAM devices.
  • To assess the suitability of RRAM-based tonotopic classification for speech recognition.

Main Methods:

  • Development of neuromorphic circuits utilizing volatile RRAM devices.
  • Implementation of a generalized stochastic device-level approach.
  • Mimicking neurobiological processes of the human auditory system, specifically cochlear signal processing.

Main Results:

  • Demonstration of logarithmic integration and tonotopic mapping of signals using RRAM circuits.
  • Successful tonotopic classification suitable for speech recognition tasks.
  • Validation of memristive devices for physical processing of temporal signals.

Conclusions:

  • Volatile RRAM devices can be effectively used to create neuromorphic circuits for advanced signal processing.
  • The developed circuits replicate key cochlear functions, enabling efficient temporal signal analysis.
  • This research paves the way for highly energy-efficient and dense neuromorphic computing systems.