24 citations · 72 across the 11 of their papers we have counts for
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cs.AR2023★ 24 cited
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers
Gamze İslamoğlu, Moritz Scherer, Gianna Paulin +4
Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and aud…
cs.AR2023★ 11 cited
FlooNoC: A Multi-Tbps Wide NoC for Heterogeneous AXI4 Traffic
Tim Fischer, Michael Rogenmoser, Matheus Cavalcante +2
Meeting the staggering bandwidth requirements of today's applications challenges the traditional narrow and serialized NoCs, which hit hard bounds on the maximum operating frequenc…