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Zhengyun Ji

2 accepted papers

2019

Learning Recurrent Binary/Ternary Weights

ICLR 2019poster

Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data. However, they are both complex and memory intensive due to their recursive nature. These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resou…

2019

The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic

NeurIPS 2019poster

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. In XNOR networks, both weights and activations are binary, bringing great benefits to spe…

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