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Found 4 papers, 1 papers with code
X-Former: In-Memory Acceleration of Transformers
Transformers have achieved great success in a wide variety of natural language processing (NLP) tasks due to the attention mechanism, which assigns an importance score for every word relative to other words in a sequence.
Efficient Ensembles of Graph Neural Networks
In this work, we leverage the unique characteristics of GNNs to overcome these overheads, creating efficient ensemble GNNs that are faster than even single models at inference time.
Specialized Transformers: Faster, Smaller and more Accurate NLP Models
We propose a Specialization framework to create optimized transformer models for a given downstream task.
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models
We propose AxFormer, a systematic framework that applies accuracy-driven approximations to create optimized transformer models for a given downstream task.
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