CORDIS Project
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This project explores biologically plausible frameworks for deep learning to improve energy efficiency in artificial intelligence. By leveraging insights from the mouse retina, it aims to develop new architectures that reduce energy consumption while maintaining performance in vision tasks.
Deep learning (DL) has recently achieved remarkable success due to the continuous growth in model sizes.
However, this growth has led to increased energy consumption.
Hardware implementation of digital DL can help reduce energy usage, but the Von Neumann architecture of current DL has hindered its practical realization.
In contrast, the brain exhibits energy-efficient multiscale spatiotemporal processing.
Biologically plausible (BiP) frameworks have emerged as alternatives to mainstream DL.
Thes…
EBERHARD KARLS UNIVERSITAET TUEBINGEN
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