This article presents an innovative reconfigurable parametric model of a digital Spiking Neuron (SN). The model is based on the classical Leaky Integrate and Fire (LIF) model with some unique modifications, allowing neuron configuration using seven different leak modes and three activation functions with dynamic threshold setting. Efficient hardware implementation of the proposed spiking neuron significantly reduces the area and power cost. The SN model is implementable requiring only 700 ASIC 2-inputs gates and is denser than IBM SN which is composed of 1272 gates. The proposed spiking neuron model can be expanded to support a larger recurrent neural network and is efficiently applied to audio applications. Performance evaluation has been carried out through a simple voice activation system design using 1000 SNs, demonstrating ultra-low power consumption of only 20uW and consuming an area of 0.03 mm2 using 28nm technology. Simulations of the proposed digital spiking neuron also demonstrate its ability to accurately replicate the behaviors of a biological neuron model.

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This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

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