Skyrmionic implementation of Spike Time Dependent Plasticity (STDP) enabled Spiking Neural Network (SNN) under supervised learning scheme
| Autor: | Udayan Ganguly, Utkarsh Saxena, Upasana Sahu, Debanjan Bhowmik, Kushaagra Goyal, Tanmay Chavan |
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| Rok vydání: | 2018 |
| Předmět: |
Spiking neural network
Quantitative Biology::Neurons and Cognition Artificial neural network Computer science Computer Science::Neural and Evolutionary Computation Supervised learning Transistor Dissipation law.invention Computer Science::Emerging Technologies Neuromorphic engineering Computer engineering law Spike (software development) Energy (signal processing) |
| Zdroj: | 2018 4th IEEE International Conference on Emerging Electronics (ICEE). |
| Popis: | Hardware implementation of Artificial Neural Network (ANN) algorithms, which are being currently used widely by the data sciences community, provides advantages of memory-computing intertwining, high speed and low energy dissipation which software implementation of the same does not have. In this paper, we simulate a spintronic hardware implementation of a third generation neural network - Spike Time Dependent Plasticity (STDP) learning enabled Spiking Neural Network (SNN), which is closer to functioning of the brain than most other ANN-s. Spin orbit torque driven skyrmionic device, driven by a transistor based circuit to enable STDP, is used as a synapse here. We use a combination of micromagnetic simulations, transistor circuit simulations and implementation of SNN algorithm in a numerical package to simulate our skyrmionic SNN. We train the skyrmionic SNN on different datasets under a supervised learning scheme and calculate the energy dissipated in updating the weights of the synapses in order to train the network. |
| Databáze: | OpenAIRE |
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