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arXiv · 2308.14711

Fast Feedforward Networks

Peter Belcak·Roger Wattenhofer
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Abstract

We break the linear link between the layer size and its inference cost by introducing the fast feedforward (FFF) architecture, a log-time alternative to feedforward networks. We demonstrate that FFFs are up to 220x faster than feedforward networks, up to 6x faster than mixture-of-experts networks, and exhibit better training properties than mixtures of experts thanks to noiseless conditional execution. Pushing FFFs to the limit, we show that they can use as little as 1% of layer neurons for inference in vision transformers while preserving 94.2% of predictive performance.

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Peter Belcak, Roger Wattenhofer. 2023-08-28. Fast Feedforward Networks. https://arxiv.org/abs/2308.14711

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