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20192023
most citedAdditive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

121 citations · 548 across the 26 of their papers we have counts for

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cs.LG2019★ 2 cited

RTN: Reparameterized Ternary Network

Yuhang Li, Xin Dong, Sai Qian Zhang +3

To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed…

cs.LG2019★ 121 cited

Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

Yuhang Li, Xin Dong, Wei Wang

We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform quantization scheme for the bell-shaped and long-tailed distribution of weights and activations in n…