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Biologically Plausible Transformers - Integrating Top-Down and Bottom-Up Signals in the Primary Vision System for Computationally Efficient Deep Learning

CORDIS · observation · Publication date unknown

Biologically Plausible Transformers - Integrating Top-Down and Bottom-Up Signals in the Primary Vision System for Computationally Efficient Deep Learning 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. These methods use bottom-up and top-down signals, incorporating feedforward and feedback mechanisms, and local objectives instead of global error. Recently, I demonstrated that a BiP opto-analog hardware can achieve competitive performance compared to digital DL for feedforward networks. However, transformers, the backbone of current DL, are challenging to implement due to the input-depe

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award

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SIGNED

region

EU

value

173847.36

unit

EUR