Abstract
Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.
| Original language | English |
|---|---|
| Title of host publication | 18th International Conference on Nanotechnology, NANO 2018 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781538653364 |
| DOIs | |
| Publication status | Published - Jan 24 2019 |
| Event | 18th International Conference on Nanotechnology, NANO 2018 - Cork, Ireland Duration: Jul 23 2018 → Jul 26 2018 |
Publication series
| Name | Proceedings of the IEEE Conference on Nanotechnology |
|---|---|
| Volume | 2018-July |
| ISSN (Print) | 1944-9399 |
| ISSN (Electronic) | 1944-9380 |
Conference
| Conference | 18th International Conference on Nanotechnology, NANO 2018 |
|---|---|
| Country | Ireland |
| City | Cork |
| Period | 7/23/18 → 7/26/18 |
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ASJC Scopus subject areas
- Bioengineering
- Electrical and Electronic Engineering
- Materials Chemistry
- Condensed Matter Physics
Cite this
Memristor-Based Synaptic Sampling Machines. / Dolzhikova, I.; Salama, K.; Kizheppatt, V.; James Pappachen, Alex.
18th International Conference on Nanotechnology, NANO 2018. IEEE Computer Society, 2019. 8626382 (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2018-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Memristor-Based Synaptic Sampling Machines
AU - Dolzhikova, I.
AU - Salama, K.
AU - Kizheppatt, V.
AU - James Pappachen, Alex
PY - 2019/1/24
Y1 - 2019/1/24
N2 - Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.
AB - Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.
UR - http://www.scopus.com/inward/record.url?scp=85062294912&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062294912&partnerID=8YFLogxK
U2 - 10.1109/NANO.2018.8626382
DO - 10.1109/NANO.2018.8626382
M3 - Conference contribution
AN - SCOPUS:85062294912
T3 - Proceedings of the IEEE Conference on Nanotechnology
BT - 18th International Conference on Nanotechnology, NANO 2018
PB - IEEE Computer Society
ER -