662 citations · 1.1k across the 11 of their papers we have counts for
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cs.NE2018★ 1 cited
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
Feiwen Zhu, Jeff Pool, Michael Andersch +2
Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network. Following r…
cs.NE2015★ 662 cited
Learning both Weights and Connections for Efficient Neural Networks
Song Han, Jeff Pool, John Tran +1
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture befor…