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The manuscript of the neural computing code section, referring to DNN + NeuroSim V1.4

References related to this tool

  1. X. Peng, S. Huang, H. Jiang, A. Lu and S. Yu, ※DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training, § IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, doi: 10.1109/TCAD.2020.3043731, 2020.
  2. X. Peng, S. Huang, Y. Luo, X. Sun and S. Yu, ※DNN+NeuroSim: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators with Versatile Device Technologies, § IEEE International Electron Devices Meeting (IEDM), 2019.
  3. X. Peng, R. Liu, S. Yu, ※Optimizing weight mapping and data flow for convolutional neural networks on RRAM based processing-in-memory architecture, § IEEE International Symposium on Circuits and Systems (ISCAS), 2019.
  4. P.-Y. Chen, S. Yu, ※Technological benchmark of analog synaptic devices for neuro-inspired architectures, § IEEE Design & Test, 2019.
  5. P.-Y. Chen, X. Peng, S. Yu, ※NeuroSim: A circuit-level macro model for benchmarking neuro-inspired architectures in online learning, § IEEE Trans. CAD, 2018.
  6. X. Sun, S. Yin, X. Peng, R. Liu, J.-S. Seo, S. Yu, ※XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks,§ ACM/IEEE Design, Automation & Test in Europe Conference (DATE), 2018.
  7. P.-Y. Chen, X. Peng, S. Yu, ※NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures, § IEEE International Electron Devices Meeting (IEDM), 2017.
  8. P.-Y. Chen, S. Yu, ※Partition SRAM and RRAM based synaptic arrays for neuro-inspired computing,§ IEEE International Symposium on Circuits and Systems (ISCAS), 2016.
  9. P.-Y. Chen, D. Kadetotad, Z. Xu, A. Mohanty, B. Lin, J. Ye, S. Vrudhula, J.-S. Seo, Y. Cao, S. Yu, ※Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip,§ IEEE Design, Automation & Test in Europe (DATE), 2015.
  10. S. Wu, et al., ※Training and inference with integers in deep neural networks,§ arXiv: 1802.04680, 2018.
  11. github.com/boluoweifenda/WAGE
  12. github.com/stevenygd/WAGE.pytorch
  13. github.com/aaron-xichen/pytorch-playground

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