A Survey on Sparsity Exploration in Transformer-Based Accelerators
A Survey on Sparsity Exploration in Transformer-Based Accelerators
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Basel: MDPI AG
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Language
English
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Publisher
Basel: MDPI AG
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Scope and Contents
Contents
Transformer models have emerged as the state-of-the-art in many natural language processing and computer vision applications due to their capability of attending to longer sequences of tokens and supporting parallel processing more efficiently. Nevertheless, the training and inference of transformer models are computationally expensive and memory i...
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Full title
A Survey on Sparsity Exploration in Transformer-Based Accelerators
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TN_cdi_proquest_journals_2819443357
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2819443357
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ISSN
2079-9292
E-ISSN
2079-9292
DOI
10.3390/electronics12102299
