CrossSim
- Sandia National Laboratories
CrossSim is a simulator for modeling neural-inspired machine learning algorithms on analog hardware, such as resistive memory crossbars. It includes noise models for reading and updating the resistances, which can be based on idealized equations or experimental data. It can also introduce noise and finite precision effects when converting values from digital to analog and vice versa. All of these effects can be turned on or off as an algorithm processes a data set and attempts to learn its salient attributes so that it can be categorized in the machine learning training/classification context. CrossSim thus allows the robustness, accuracy, and energy usage of a machine learning algorithm to be tested on simulated hardware.
- Short Name / Acronym:
- CrossSim
- Project Type:
- Open Source, No Publicly Available Repository
- Site Accession Number:
- 7607; SCR# 2128
- Software Type:
- Scientific
- License(s):
- Other
- Programming Language(s):
- Python.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOEPrimary Award/Contract Number:AC04-94AL85000
- DOE Contract Number:
- AC04-94AL85000
- Code ID:
- 73085
- OSTI ID:
- 1373357
- Country of Origin:
- United States
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