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GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework

, , , , and . (2017)cite arxiv:1705.09283Comment: 11 pages, 13 figures.

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Endowing Spiking Neural Networks with Homeostatic Adaptivity for APS-DVS Bimodal Scenarios., , and . ICMI Companion, page 12-17. ACM, (2022)Target Controllability of Two-Layer Multiplex Networks Based on Network Flow Theory., , , , , , and . IEEE Trans. Cybern., 51 (5): 2699-2711 (2021)HASP: Hierarchical Asynchronous Parallelism for Multi-NN Tasks., , , , , , , , , and 1 other author(s). IEEE Trans. Computers, 73 (2): 366-379 (February 2024)GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework, , , , and . (2017)cite arxiv:1705.09283Comment: 11 pages, 13 figures.Efficient GCN Deployment with Spiking Property on Spatial-Temporal Neuromorphic Chips., , , and . ICONS, page 21:1-21:8. ACM, (2023)Real-time Tracking Based on Neuromrophic Vision., , and . CoRR, (2015)ARLIF: A Flexible and Efficient Recurrent Neuronal Model for Sequential Tasks., , , , and . HBAI@IJCAI, volume 1369 of Communications in Computer and Information Science, page 1-13. Springer, (2020)Bridging adversarial samples and adversarial networks., , , , and . CoRR, (2019)Real-time tracking based on neuromrophic vision., , and . NVMTS, page 1-7. IEEE, (2015)GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework., , , , and . Neural Networks, (2018)