Data files for In situ training of feedforward and recurrent convolutional memristor networks
- 1. Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA
- 2. Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA
- 3. Hewlett Packard Labs, Hewlett Packard Enterprise, Palo Alto, CA 94304, USA
- 4. Air Force Research Laboratory, Information Directorate, Rome, NY 13441, USA
- 5. Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USA
- 6. Institue of Microelectronics, Tsinghua University, Beijing 100084, China
- 7. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
Description
MATLAB data files for the manuscript "In situ training of feedforward and recurrent convolutional memristor networks" published on Nature Machine Intelligence, 2019.
The MATLAB data file "exp_mnist.mat" consists of all experimental data on implementing the convolutional neural network with the 1-transistor 1-memristor array that is used for plotting the Figure 1 and 2 of the manuscript.
The MATLAB data file "exp_mnistsequence.mat" consists of all experimental data on implementing the convolutional long short-term memory network on the 1-transistor 1-memristor array that is used for plotting the Figure 3 and 4 of the manuscript.
The code that generated these data files are provided by the link within the manuscript. Alternatively, the code can be accessed via https://github.com/zhongruiwang/memristorCNN.
Files
Additional details
Related works
- Is compiled by
- https://github.com/zhongruiwang/memristorCNN (URL)
- Is supplement to
- 10.1038/s42256-019-0089-1 (DOI)
References
- Wang, Zhongrui et al. (2019) In situ training of feed-forward and recurrent convolutional memristor networks, Nat. Mach. Intell.