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Found 3 papers, 1 papers with code
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
The emergence of XNOR networks seek to reduce the model size and computational cost of neural networks for their deployment on specialized hardware requiring real-time processes with limited hardware resources.
Learning to Skip Ineffectual Recurrent Computations in LSTMs
This observation suggests that a large fraction of the recurrent computations are ineffectual and can be avoided to speed up the process during the inference as they involve noncontributory multiplications/accumulations with zero-valued states.
Learning Recurrent Binary/Ternary Weights
On the software side, we evaluate the performance (in terms of accuracy) of our method using long short-term memories (LSTMs) on various sequential models including sequence classification and language modeling.
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