Hybrid convolutional neural network yields energy-efficient approach with analog first layer

Oscillatory retinal neuron networks don’t require external voltage sources and show comparable performance to cutting-edge GPU-based convolutional neural networks, for energy costs thousands of times less than current approaches.

Authors

Publication Details

Journal
Scilight
Published
2026-06-16
DOI
https://doi.org/10.1063/10.0044206
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Hybrid convolutional neural network yields energy-efficient approach with analog first layer

Adam Liebendorfer
Scilight
Advanced Memory and Neural Computing
article

Hybrid convolutional neural network yields energy-efficient approach with analog first layer

Adam Liebendorfer
article en

Abstract

Oscillatory retinal neuron networks don’t require external voltage sources and show comparable performance to cutting-edge GPU-based convolutional neural networks, for energy costs thousands of times less than current approaches.

ScilightVol. 2026(25)
Affordable and clean energy
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
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Hybrid convolutional neural network yields energy-efficient approach with analog first layer — Adam Liebendorfer · Scilight (2026) | TGRS Research Map | TGRS