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NeuroSEE: A Neuromorphic Energy-Efficient Processing Framework for Visual Prostheses
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DOI：10.1109/JBHI.2022.3172306.png)
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Abstract
En 中文
Visual prostheses with both comprehensive visual signal processing capability and energy efficiency are becoming increasingly demanded in the age of intelligent personal healthcare, particularly with the rise of wearable and implantable devices. To address this trend, we propose NeuroSEE, a neuromorphic energy-efficient processing framework that combines a spike representation encoding technique and a bio-inspired processing method. This framework first utilizes sparse spike trains to represent visual information, and then a bio-inspired spiking neural network (SNN) is adopted to process the spike trains. The SNN model makes use of an IF neuron with multiple spike-firing rates to decrease the energy consumption without compensating for prediction performance. The experimental results indicate that when predicting the response of the primary visual cortex, the framework achieves a state-of-the-art Pearson correlation coefficient performance. Spike-based recording and processing methods simplify the storage and transmission of redundant scene information and complex calculation processes. It could reduce power consumption by 15 times compared with the existing Convolutional neural network (CNN) processing framework. The proposed NeuroSEE framework predicts the response of the primary visual cortex in an energy efficient manner, making it a powerful tool for visual prostheses.
Keywords:
Visual prosthesis
Visualization
Biological system modeling
Retina
Predictive models
Image restoration
Image edge detection
Visual prostheses
bio-inspired processing
spiking neural network
Age-related macular degeneration
retinitis pigmentosa
wearable devices
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