Computational Modeling of Ganglion Cell Bicolor Opponent Receptive Fields and FPGA Adaptation for Parallel Arrays
Hui Wei, Wenbo Yao
TL;DR
This paper models visual signal processing in the retina and implements it on FPGAs to achieve low power and high parallelism for vision systems.
Contribution
A novel FPGA-based parallel computing model for retinal visual pathways with low energy consumption.
Findings
The FPGA model achieves high parallelism (600) and a maximum frequency of 200 MHz.
Each receptive field model consumes only 0.142 W of power.
The design supports efficient information transmission for vision systems in small devices.
Abstract
The biological system is not a perfect system, but it is a relatively complete system. It is difficult to realize the lower power consumption and high parallelism that characterize biological systems if lower-level information pathways are ignored. In this paper, we focus on the K, M and P pathways of visual signal processing from the retina to the lateral geniculate nucleus (LGN). We model the visual system at a fine-grained level to ensure efficient information transmission while minimizing energy use. We also implement a circuit-level distributed parallel computing model on FPGAs. The results show that we are able to transfer information with low energy consumption and high parallelism. The Artix-7 family of xc7a200tsbv484-1 FPGAs can reach a maximum frequency of 200 MHz and a maximum parallelism of 600, and a single receptive field model consumes only 0.142 W of power. This can be…
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Taxonomy
TopicsRetinal Development and Disorders · Photoreceptor and optogenetics research · Neuroscience and Neural Engineering