  Deep Neural Networks (DNNs) have been successfully implemented across various
signal processing fields, resulting in significant enhancements in performance.
However, DNNs generally require substantial computational resources, leading to
significant economic costs and posing challenges for their deployment on
resource-constrained edge devices. In this study, we take advantage of spiking
neural networks (SNNs) and quantization technologies to develop an
energy-efficient and lightweight neuromorphic signal processing system. Our
system is characterized by two principal innovations: a threshold-adaptive
encoding (TAE) method and a quantized ternary SNN (QT-SNN). The TAE method can
efficiently encode time-varying analog signals into sparse ternary spike
trains, thereby reducing energy and memory demands for signal processing.
QT-SNN, compatible with ternary spike trains from the TAE method, quantifies
both membrane potentials and synaptic weights to reduce memory requirements
while maintaining performance. Extensive experiments are conducted on two
typical signal-processing tasks: speech and electroencephalogram recognition.
The results demonstrate that our neuromorphic signal processing system achieves
state-of-the-art (SOTA) performance with a 94% reduced memory requirement.
Furthermore, through theoretical energy consumption analysis, our system shows
7.5x energy saving compared to other SNN works. The efficiency and efficacy of
the proposed system highlight its potential as a promising avenue for
energy-efficient signal processing.
