TY - GEN
T1 - Reservoir Computing System using Biomolecular Memristor
AU - Hossain, Md Razuan
AU - Najem, Joseph S.
AU - Rahman, Tauhidur
AU - Hasan, Md Sakib
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/28
Y1 - 2021/7/28
N2 - Reservoir Computing (RC) is a highly efficient machine learning algorithm specially suited for processing temporal dataset. RC system extracts features from input by projecting them into a high dimensional space. A major advantage of RC framework is that it only requires the readout layer to be trained which significantly reduces the training cost for complex temporal data. In recent years, memristors have become extremely popular in neuromorphic applications due to their attractive analogy to biological synapses. Alamethicin-doped, synthetic biomembrane can emulate key synaptic functions due to its volatile memristive property which can enable learning and computation. In contrast to its solid-state counterparts, this two-terminal biomolecular memristor features similar structure, switching mechanism, and ionic transport modality as biological synapses while consuming considerably lower power. In this work, we have shown biomolecular memristor-based reservoir system to solve tasks such as classification and time-series analysis in a simulation based environment. Our work may pave the way towards highly energy efficient and biocompatible memristor-based reservoir computing systems capable of handling complex temporal tasks in hardware in the near future.
AB - Reservoir Computing (RC) is a highly efficient machine learning algorithm specially suited for processing temporal dataset. RC system extracts features from input by projecting them into a high dimensional space. A major advantage of RC framework is that it only requires the readout layer to be trained which significantly reduces the training cost for complex temporal data. In recent years, memristors have become extremely popular in neuromorphic applications due to their attractive analogy to biological synapses. Alamethicin-doped, synthetic biomembrane can emulate key synaptic functions due to its volatile memristive property which can enable learning and computation. In contrast to its solid-state counterparts, this two-terminal biomolecular memristor features similar structure, switching mechanism, and ionic transport modality as biological synapses while consuming considerably lower power. In this work, we have shown biomolecular memristor-based reservoir system to solve tasks such as classification and time-series analysis in a simulation based environment. Our work may pave the way towards highly energy efficient and biocompatible memristor-based reservoir computing systems capable of handling complex temporal tasks in hardware in the near future.
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U2 - 10.1109/NANO51122.2021.9514305
DO - 10.1109/NANO51122.2021.9514305
M3 - Conference contribution
AN - SCOPUS:85114961901
T3 - Proceedings of the IEEE Conference on Nanotechnology
SP - 116
EP - 119
BT - NANO 2021 - 21st IEEE International Conference on Nanotechnology, Proceedings
PB - IEEE Computer Society
T2 - 21st IEEE International Conference on Nanotechnology, NANO 2021
Y2 - 28 July 2021 through 30 July 2021
ER -