CrossSim: Crossbar Simulator

About CrossSim

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CrossSim is a GPU-accelerated, Python-based crossbar simulator designed to model analog in-memory computing for neural networks and linear algebra applications. It is an accuracy simulator and co-design tool that was developed to address how analog hardware effects in resistive crossbars impact the quality of the algorithm solution. CrossSim provides a special interface to model analog accelerators for neural network inference and training, and also has an API that allows different algorithms to be built on resistive memory array building blocks.

CrossSim can model device and circuit non-idealities such as arbitrary programming errors, conductance drift, cycle-to-cycle read noise, and precision loss in analog-to-digital conversion (ADC). It also uses a fast, internal circuit simulator to model the effect of parasitic metal resistances on accuracy. For neural network inference, it can simulate accelerators with significant parameterizability at the system architecture level and can be used to explore how design choices such as weight bit slicing, negative number representation scheme, ADC ranges, and array size affect the sensitivity to these analog errors. CrossSim can be accelerated on CUDA GPUs, and inference simulations have been run on large-scale deep neural networks such as ResNet50 on ImageNet.

For neural network training accelerators, CrossSim can generate lookup tables of device behavior from experimental data. These lookup tables are then used to realistically simulate the accuracy impact of arbitrarily complex conductance update characteristics, including write nonlinearity, write asymmetry, write stochasticity, and device-to-device variability.

CrossSim does not explicitly model the energy, area, or speed of analog accelerators.

Access CrossSim

CrossSim is developed and maintained on GitHub.


More extensive documentation of CrossSim Training and the neural core API will be released in a future update.

Selected Publications Using CrossSim

Contact Us

For questions, feature requests, bug reports, or suggestions on the CrossSim software, please submit a new issue through GitHub. For other questions or if you would like to contribute to the source code, please e-mail T. Patrick Xiao, Christopher Bennett, or Sapan Agarwal.