Title: Self-aware artificial auditory neuron with a triboelectric sensor for spike-based neuromorphic hardware
Abstract: Auditory organs can detect and process sounds with the help of the organ of Corti located in the cochlea. The energy efficiency of biological processing for an input sound signal is very high due to the spike-based auditory neurons in auditory organs. Here, inspired by the biological auditory system, a self-aware artificial auditory neuron module is constructed by serially connecting a triboelectric nanogenerator (TENG) and a bi-stable resistor (biristor). The TENG serves as a sound sensor and a current source to feed current input so as to awaken the biristor, which acts as a device-level neuron that is dissimilar to a traditional circuit-based neuron. The proposed self-aware artificial neuron module simultaneously detects the sound pressure level (SPL) and encodes it into a spike form, after which it transmits these data to an artificial synapse as an input neuron for a spiking neural network (SNN). Like an auditory organ, the spiking frequency of the bio-inspired artificial neuron increases when the SPL increases. In addition, the self-aware artificial auditory system with a single-layer perceptron (SLP) for SNN is demonstrated for musical pitch classification. This artificial auditory system is configured by combining two artificial auditory neuron modules and four metal-oxide-semiconductor field-effect transistor (MOSFET) synapses. The biristor neuron and the MOSFET synapse are structurally identical but electrically different. Two artificial auditory neuron modules correspond to two different frequencies. This artificial auditory system distinguishes two sounds from a piano. It also identifies two similar sounds from a cello and a violin. The proposed artificial SNN-based auditory system is advantageous for low power consumption due to the event-driven spiking transmission scheme. The improved energy efficiency with the SNN as described here is attributed to the self-aware sensing capability for sound signals. Therefore, the self-aware SNN auditory system given its low power consumption is promising for a remote sensor, a wireless sensor, and for an Internet of Things (IoT) sensor system.
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