TY - GEN
T1 - Domain Wall Motion-based XOR-like Activation Unit with A Programmable Threshold
AU - Deb, Suman
AU - Vatwani, Tarun
AU - Chattopadhyay, Anupam
AU - Basu, Arindam
AU - Fong, Xuanyao
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/10/10
Y1 - 2018/10/10
N2 - Spintronic devices promise an excellent opportunity for implementing ultra-low power neuromorphic platforms due to the inherent correspondence between their physical characteristics and the required neuronal, synaptic functionalities. Neuromorphic circuits using domain wall motion-based threshold neurons have been demonstrated in previous studies. However, threshold neurons are unable to realize linearly inseparable functions. Our work addresses this challenge by proposing a new domain wall motion-based neural activation unit with XOR-like activation function. We also develop a new learning algorithm for neurons with this activation unit. Offline training is performed on real-world datasets from the UCI machine learning repository. Neuromorphic circuits corresponding to these datasets are also simulated. The results suggest femto-Joule range energy consumption of a neuron with the proposed activation unit and 1.08× -1.82× lower misclassification rate (MCR) of the proposed algorithm in comparison to the traditional perceptron learning algorithm.
AB - Spintronic devices promise an excellent opportunity for implementing ultra-low power neuromorphic platforms due to the inherent correspondence between their physical characteristics and the required neuronal, synaptic functionalities. Neuromorphic circuits using domain wall motion-based threshold neurons have been demonstrated in previous studies. However, threshold neurons are unable to realize linearly inseparable functions. Our work addresses this challenge by proposing a new domain wall motion-based neural activation unit with XOR-like activation function. We also develop a new learning algorithm for neurons with this activation unit. Offline training is performed on real-world datasets from the UCI machine learning repository. Neuromorphic circuits corresponding to these datasets are also simulated. The results suggest femto-Joule range energy consumption of a neuron with the proposed activation unit and 1.08× -1.82× lower misclassification rate (MCR) of the proposed algorithm in comparison to the traditional perceptron learning algorithm.
KW - ANN
KW - domain wall motion
KW - learning algorithm
KW - memristive crossbar array
KW - Neuromorphic computing
KW - non-linearly separable function
KW - threshold function
UR - https://www.scopus.com/pages/publications/85056527399
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85056527399&origin=recordpage
U2 - 10.1109/IJCNN.2018.8489146
DO - 10.1109/IJCNN.2018.8489146
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509060146
VL - 2018-July
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PB - IEEE
T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018
Y2 - 8 July 2018 through 13 July 2018
ER -