Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors
Artificial neurons form the core of neuromorphic computing which is emerging as an alternative for the von Neumann computing architecture. However, existing neuron architectures still lack in area efficiency, especially considering the huge size of modern neural networks requiring millions of neuron...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Journal of the Electron Devices Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10391069/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583246186020864 |
|---|---|
| author | Kannan Udaya Mohanan Seyed Mehdi Sattari-Esfahlan Eou-Sik Cho Chang-Hyun Kim |
| author_facet | Kannan Udaya Mohanan Seyed Mehdi Sattari-Esfahlan Eou-Sik Cho Chang-Hyun Kim |
| author_sort | Kannan Udaya Mohanan |
| collection | DOAJ |
| description | Artificial neurons form the core of neuromorphic computing which is emerging as an alternative for the von Neumann computing architecture. However, existing neuron architectures still lack in area efficiency, especially considering the huge size of modern neural networks requiring millions of neurons. Here, we report on a compact leaky integrate and fire (LIF) neuron circuit based on graphene memristor device. The LIF circuit exhibits various biological properties like threshold control, leaky integration and reset behavior. Circuit parameters like the synaptic resistance and membrane capacitance act as additional control parameters whereby the spike frequency of the circuit can be effectively controlled. Uniquely, the circuit exhibits biologically realistic frequencies as low as 286 Hz. The results suggest the suitability of this compact and biorealistic LIF neuron circuit towards future bioinspired computing systems |
| format | Article |
| id | doaj-art-a6a157adf46d439185c939df20b96180 |
| institution | Kabale University |
| issn | 2168-6734 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of the Electron Devices Society |
| spelling | doaj-art-a6a157adf46d439185c939df20b961802025-01-29T00:00:08ZengIEEEIEEE Journal of the Electron Devices Society2168-67342024-01-0112889510.1109/JEDS.2024.335282710391069Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene MemristorsKannan Udaya Mohanan0https://orcid.org/0000-0002-1270-4596Seyed Mehdi Sattari-Esfahlan1Eou-Sik Cho2https://orcid.org/0000-0002-8145-3516Chang-Hyun Kim3https://orcid.org/0000-0002-7112-6335School of Electronic Engineering, Gachon University, Seongnam, South KoreaInstitute for Microelectronics, Technical University of Vienna, Vienna, AustriaSchool of Electronic Engineering, Gachon University, Seongnam, South KoreaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaArtificial neurons form the core of neuromorphic computing which is emerging as an alternative for the von Neumann computing architecture. However, existing neuron architectures still lack in area efficiency, especially considering the huge size of modern neural networks requiring millions of neurons. Here, we report on a compact leaky integrate and fire (LIF) neuron circuit based on graphene memristor device. The LIF circuit exhibits various biological properties like threshold control, leaky integration and reset behavior. Circuit parameters like the synaptic resistance and membrane capacitance act as additional control parameters whereby the spike frequency of the circuit can be effectively controlled. Uniquely, the circuit exhibits biologically realistic frequencies as low as 286 Hz. The results suggest the suitability of this compact and biorealistic LIF neuron circuit towards future bioinspired computing systemshttps://ieeexplore.ieee.org/document/10391069/artificial neuronSPICEneuromorphic computing |
| spellingShingle | Kannan Udaya Mohanan Seyed Mehdi Sattari-Esfahlan Eou-Sik Cho Chang-Hyun Kim Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors IEEE Journal of the Electron Devices Society artificial neuron SPICE neuromorphic computing |
| title | Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors |
| title_full | Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors |
| title_fullStr | Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors |
| title_full_unstemmed | Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors |
| title_short | Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors |
| title_sort | optimization of leaky integrate and fire neuron circuits based on nanoporous graphene memristors |
| topic | artificial neuron SPICE neuromorphic computing |
| url | https://ieeexplore.ieee.org/document/10391069/ |
| work_keys_str_mv | AT kannanudayamohanan optimizationofleakyintegrateandfireneuroncircuitsbasedonnanoporousgraphenememristors AT seyedmehdisattariesfahlan optimizationofleakyintegrateandfireneuroncircuitsbasedonnanoporousgraphenememristors AT eousikcho optimizationofleakyintegrateandfireneuroncircuitsbasedonnanoporousgraphenememristors AT changhyunkim optimizationofleakyintegrateandfireneuroncircuitsbasedonnanoporousgraphenememristors |