10 citations · 10 across the 2 of their papers we have counts for
3 papers
cs.CV2023
Learnable Heterogeneous Convolution: Learning both topology and strength
Rongzhen Zhao, Zhenzhi Wu, Qikun Zhang
Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficie…
cs.LG2020★ 10 cited
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin +3
Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Thanks to the bio-p…
cs.LG2017
GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework
Lei Deng, Peng Jiao, Jing Pei +2
There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end,…