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A Novel Double-Threshold Neural Classifier for Non-Linearly Separable Applications
Kashif, Mohd Rahman, Syed Atiqur Ansari, Mohammad Samar
Kashif, Mohd
Rahman, Syed Atiqur
Ansari, Mohammad Samar
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2023-03-28
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Classification of data finds applications in various engineering and scientific problems. When real-time operation is desired, hardware solutions tend to be more amenable as compared to algorithmic/heuristic solutions. This paper presents a novel current-mode dual-threshold neuron designed and implemented at 32nm CMOS technology node. Subsequently, a current-mode double-threshold classifier is presented which is capable of classifying input patterns of non-linearly separable problems. Thereafter, application of the current-mode dual-threshold neuron in the realization of the XOR function using only a single neural unit is discussed. The proposed neuron as well as both the applications discussed are capable of operating from sub-1V power supplies. Computer simulations using HSPICE yield promising results with the values of delay and power consumption estimated to be lower than existing circuits.
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Kashif, M., Rahman, S. A., & Ansari, M. S. (2022). A novel double-threshold neural classifier for non-linearly separable applications. 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2022, pp. 1546-1550. https://doi.org/10.1109/ICAC3N56670.2022.10074054
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