ETH Zurich Researchers Introduce UltraFastBERT: A BERT Variant that Uses 0.3% of its Neurons during Inference while Performing on Par with Similar BERT Models

The development of UltraFastBERT by researchers at ETH Zurich addressed the problem of reducing the number of neurons used during inference while maintaining performance levels similar to other models. It was achieved through fast feedforward networks (FFFs), which resulted in a significant speedup compared to baseline implementations. The existing methods have been supported by the […]

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