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MASTISK acronym for MAchine-learning and Synaptic-plasticity Technology Integrated Simulation frameworK is an open-source versatile and flexible tool developed in MATLAB for design exploration of dedicated neuromorphic hardware by researchers of the Non-Volatile Memory Research group at the Indian Institute of Technology - Delhi (http://web.iitd.ac.in/~manansuri/).
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MASTISK acronym for MAchine-learning and Synaptic-plasticity Technology Integrated Simulation frameworK is an open-source versatile and flexible tool developed in MATLAB for design exploration of dedicated neuromorphic hardware by researchers of the Non-Volatile Memory Research group at the Indian Institute of Technology - Delhi (http://web.iitd.ac.in/~manansuri/).
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MASTISK supports nanodevices and hybrid CMOS-nanodevice circuits. It has a hierarchical organization capturing details at the level of devices, circuits (i.e. neurons or activation functions, synapses or weights) and architectures (i.e. topology, learning-rules, algorithms).
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In Sanskrit etymology the word MASTISK (pronounced as mas-tea-she-k) means 'brain'.
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Current version provides user-friendly interface for design and simulation of spiking neural networks (SNN) powered by spatio-temporal learning rules such as Spike-Timing Dependent Plasticity (STDP). Users can provide network definition as a simple input parameter file and the framework is capable of performing automated learning/inference simulations.
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MASTISK supports nanodevices and hybrid CMOS-nanodevice circuits. It has a hierarchical organization capturing details at the level of devices, circuits (i.e. neurons or activation functions, synapses or weights) and architectures (i.e. topology, learning-rules, algorithms).
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Case studies, codes, relevant publications, scripts, manuals, examples, and licensing information will be updated from time to time.
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Current version provides user-friendly interface for design and simulation of spiking neural networks (SNN) powered by spatio-temporal learning rules such as Spike-Timing Dependent Plasticity (STDP). Users can provide network definition as a simple input parameter file and the framework is capable of performing automated learning/inference simulations.
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The developers assume no liability of any kind financial, legal or otherwise arising out of the usage of the framework or the results produced by using it.
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Case studies, codes, relevant publications, scripts, manuals, examples, and licensing information will be updated from time to time.
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For access to code please send an email request to, indicating your 1.name, 2.Affiliation and 3.purpose of use : <manansuri@ee.iitd.ac.in> or <nvmresearchiitd@gmail.com> |
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The developers assume no liability of any kind financial, legal or otherwise arising out of the usage of the framework or the results produced by using it.
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If you use MASTISK, we request you to cite our work by copy-pasting the following exact citation:
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M. Suri et. al, MASTISK (Machine learning and synaptic plasticity technology integration simulation framework), 2018, https://gitlab.com/NVM_IITD_Research/MASTISK/wikis/home |
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