CrossSim is a crossbar simulator designed to model resistive memory crossbars for both neuromorphic computing and (in a future release) digital memories. It provides a clean python API so that different algorithms can be built upon crossbars while modeling realistic device properties and variability. The crossbar can be modeled using multiple fast approximate numerical models including both analytic noise models as well as experimentally derived lookup tables. A slower, but more accurate circuit simulation of the devices using the parallel spice simulator Xyce is also being developed and will be included in a future release.
DownloadDownload the user manual here: CrossSim_manual.pdf
Download CrossSim v0.2 here: cross_sim-0.2.0.tar.gz
Download example scripts here: examples.tar.gz
Please email Sapan Agarwal for any questions or if you would like to contribute to the source code: firstname.lastname@example.org.
Selected Publications Using CrossSim
- S. Agarwal, R. B. Jacobs-Gedrim, A. H. Hsia, D. R. Hughart, E. J. Fuller, A. A. Talin, C. D. James, S. J. Plimpton, and M. J. Marinella, "Achieving Ideal Accuracies in Analog Neuromorphic Computing Using Periodic Carry," in 2017 IEEE Symposium on VLSI Technology Kyoto, Japan, 2017.
- Y. van de Burgt, E. Lubberman, E. J. Fuller, S. T. Keene, G. C. Faria, S. Agarwal, M. J. Marinella, A. Alec Talin, and A. Salleo, "A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing," Nat Mater, Letter vol. 16, no. 4, pp. 414-418, 2017
- E. J. Fuller, F. E. Gabaly, F. Léonard, S. Agarwal, S. J. Plimpton, R. B. Jacobs-Gedrim, C. D. James, M. J. Marinella, and A. A. Talin"Li-Ion Synaptic Transistor for Low Power Analog Computing," Advanced Materials, vol. 29, no. 4, p. 1604310, 2017.
- S. Agarwal, S. J. Plimpton, D. R. Hughart, A. H. Hsia, I. Richter, J. A. Cox, C. D. James, and M. J. Marinella, "Resistive memory device requirements for a neural algorithm accelerator," in 2016 International Joint Conference on Neural Networks (IJCNN), 2016, pp. 929-938.