39 citations · 103 across the 8 of their papers we have counts for
3 papers · 1 filter
Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
Jong Hoon Shin, Ali Shafiee, Ardavan Pedram +3
This paper examines the design space trade-offs of DNNs accelerators aiming to achieve competitive performance and efficiency metrics for all four combinations of dense or sparse a…
Learned Token Pruning for Transformers
Sehoon Kim, Sheng Shen, David Thorsley +4
Deploying transformer models in practice is challenging due to their inference cost, which scales quadratically with input sequence length. To address this, we present a novel Lear…
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
Hamzah Abdel-Aziz, Ali Shafiee, Jong Hoon Shin +2
In this paper, we propose a mixed-precision convolution unit architecture which supports different integer and floating point (FP) precisions. The proposed architecture is based on…