Spike‐Enabled Audio Learning in Multilevel Synaptic Memristor Array‐Based Spiking Neural Network

Abstract

Speech recognition involves the ability to learn the audios which are closely related to event sequence. Although speech recognition has been widely implemented in software neural networks, a hardware implementation based on energy efficient computing architecture is still missing. Herein, W/MgO/SiO2/Mo memristor arrays with multilevel resistance states are fabricated, where the weights of the artificial synapses in the memristor array can be tuned precisely by voltage pulses. Based on the array, speech recognition in memristive spiking neural networks (SNNs) with improved supervised tempotron algorithm on Texas Instruments digit sequences (TIDIGITS) dataset is conducted, demonstrating software‐comparable accuracy for speech recognition in the memristive SNN. It is envisioned that such memristive SNNs can pave the way to building a bioinspired spike‐based neuromorphic system for audio learning.

Keywords

• long-term plasticity
• memristors
• speech recognition
• spiking neural networks