The MLP+NeuroSim framework was developed by Prof. Shimeng Yu's group (Georgia Institute of Technology). The model is made publicly available on a non-commercial basis. Copyright of the model is maintained by the developers, and the model is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International Public License

This is the released version 3.0 (Mar. 1st, 2019) for the tool. This version extends the algoritihm weights from (0,1) in V2.0 to (-1,1) in V3.0. Besides, more optimization methods such as momentum method, Adagrad, RMSprop, Adam are added. The digital eNVMs (e.g. STT-MRAM) based synaptical array supports parallel read-out is introduced to reduce latency. Hybrid precision synapses with good linearity are added to provide better training accuracy

Developers: Pai-Yu Chen, Xiaochen Peng and Yandong Luo.

If you have logistic questions or comments on the model, please contact Prof. Shimeng Yu ([email protected]), and if you have technical questions or comments, please contact Anni Lu([email protected]) or Yandong Luo ([email protected]).

This research is supported by NSF CAREER award, NSF/SRC E2CDA program, and ASCENT, one of the SRC/DARPA JUMP centers.

If you use the tool or adapt the tool in your work or publication, you are required to cite the following reference:

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, San Francisco, USA.

If you use the new features in this version (e.g. the advanced optimization methods such as momentum, Adagrad, RMSprop, Adam; the hybrid precision synapse such as 3T1C+NVM or 2T1F), you are required to cite the following reference:

Y. Luo, X. Peng and S. Yu, " MLP+NeuroSimV3.0: Improving On-chip Learning Performance with Device to Algorithm Optimizations," 2019 ACM the International Conference on Neuromorphic Systems (ICONS), ACM, New York, NY, USA.

1. MATLAB fitting script: nonlinear_fit.m
2. Nonlinearity-to-A table: Documents/Nonlinearity-NormA.htm
3. MNIST data: MNIST_data.zip
4. Manual: Documents/Manual.pdf
5. MLP Simulator (+NeuroSim): the rest of the files

1. Get the tool from GitHub

git clone https://github.com/neurosim/MLP_NeuroSim_V3.0.git

2. Extract MNIST_data.zip to it’s current directory

unzip MNIST_data.zip

3. Compile the codes

make

For the usage of this tool, please refer to the manual.

Updates on Jan. 20th, 2020:

1. In sub-array, use linear-region transistor in MUX, Switch Matrix and across-transistor in array.
2. Calibrate FinFET technology library (<20nm)

Updates on Aug. 21st, 2021:

1. Add the hybrid precision synapse (3T1C+NVM, 2T1F) as device option. You can find more information on this in the ICONS'19 paper mentioned above.
2. Add batch size as an input parameter (in param.cpp).

1. Y. Luo, X. Peng and S. Yu, " MLP+NeuroSimV3.0: Improving On-chip Learning Performance with Device to Algorithm Optimizations," 2019 ACM the International Conference on Neuromorphic Systems (ICONS), ACM, New York, NY, USA.
2. P.-Y. Chen, S. Yu, "Technological benchmark of analog synaptic devices for neuro-inspired architectures," IEEE Design & Test, 2019.
3. P.-Y. Chen, X. Peng, S. Yu, “NeuroSim: A circuit-level macro model for benchmarking neuro-inspired architectures in online learning,” IEEE Trans. CAD,vol. 37, no. 12, pp. 3067-3080, 2018.
4. 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, San Francisco, USA.
5. P.-Y. Chen, X. Peng, S. Yu, "System-level benchmark of synaptic device characteristics for neuro-inspired computing," IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S) 2017, San Francisco, USA.
6. 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, Montreal, Canada.