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The identification of threshold function (TF), which determines whether a Boolean function can be represented by an linear threshold logic gate (LTG) or not, is a fundamental but important task in the theories of threshold logic. In this paper, we propose a more efficient and effective algorithm of TF identification by constructing the system of irredundant inequalities and adjusting the weight assignment comprehensively. This is the first non-ILP-based approach that is able to identify all the eight-input TFs. The experimental results demonstrated that the proposed approach is more effective than all the existing non-ILP-based approaches and the LTGs obtained by the proposed approach are optimal for near 100% cases. For TFs with 9–15 inputs, the proposed approach can identify 100 000 randomly generated TFs as well in a reasonable CPU time. [ABSTRACT FROM AUTHOR]

Memristor, which is a two-terminal nanodevice, widely used in various fields, e.g., machine learning and neuromorphic systems, has attracted much attention these years. Memristor can also be used to realize an implication logic gate and thus logic circuits. However, the fanouts in a memristor-based logic circuit have some constraints and need to be processed with special care. On the other hand, in addition to the number of memristors, the number of operational pulses is another metric to measure the quality of a memristor-based logic circuit. Hence, in this paper, we propose a synthesis algorithm to deal with the fanout problems in memristor-based logic circuits using implication logic gates for having a minimal number of operational pulses. We conducted experiments on a set of MCNC benchmarks. The experimental results show that the proposed algorithm can reduce 29% operational pulses and 36% memristor count on average compared with the state-of-the-art. [ABSTRACT FROM AUTHOR]

Single-electron transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore’s law due to its ultralow-power consumption. Previous works proposed mapping approaches to implement Boolean functions on SET arrays. However, these approaches were based on an ideal assumption that the SET arrays are defect-free. Recently, a diagnosis method was proposed targeting at defective SET arrays. However, the approach was static, such that the performance is inefficient. As a result, in this paper, we propose a dynamic diagnosis approach that can efficiently identify the locations and the types of the defects in the SET arrays. The experimental results show that the proposed dynamic diagnosis approach can achieve the same results as the previous work with much less CPU time on a set of benchmarks. Furthermore, the proposed method spent a few seconds while the previous work exceeded the CPU time limit of 3600 s on some benchmarks. [ABSTRACT FROM PUBLISHER]