Name
Abstract | ||
|---|---|---|
In this paper, we present MASTISK (MAchine-learning and Synaptic-plasticity Technology Integrated Simulation frameworK). MASTISK is an open-source versatile and flexible tool developed in MATLAB for design exploration of dedicated neuromorphic hardware using nanodevices and hybrid CMOS-nanodevice circuits. MASTISK 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). In the current version, MASTISK 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 may provide network definition as a simple input parameter file and the framework is capable of performing automated learning/inference simulations. Validation case-studies of the proposed open source simulator will be published in the proceedings of IJCNN 2018. The proposed framework offers new functionalities, compared to similar simulation tools in literature, such as: (i) arbitrary synaptic circuit modeling capability with both identical and non-identical stimuli, (ii) arbitrary spike modeling, and (iii) nanodevice based neuron emulation. The code of MASTISK is available on request at: https://gitlab.com/NVM IITD Research/MASTISK/wikis/home |
Year | Venue | DocType |
|---|---|---|
2018 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1804.00912 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tinish Bhattacharya | 1 | 0 | 0.34 |
| vivek parmar | 2 | 8 | 5.42 |
| manan suri | 3 | 10 | 7.84 |