Abstract
With the increase in communication bandwidth and frequency, the development level of communication technology is also constantly developing. The scale of the Internet of Things (IoT) has shifted from single point-to-point communication to mesh communication between sensors. However, the large sensors serving the infrastructure place a burden on real-time monitoring, data transmission, and even data analysis. The information processing method is experimentally demonstrated with a non-linear Schmitt trigger oscillator. A neuronally inspired concept called reservoir computing has been implemented. The synchronization frequency prediction tasks are utilized as benchmarks to reduce the computational load. The oscillator's oscillation frequency is affected by the sensor input, further affecting the storage pattern of the oscillatory neural network. This paper proposes a method of information processing by training and modulating the weights of the intrinsic electronic neural network to achieve the next step prediction. The effects on the frequency of a single oscillator in a coupled oscillatory neural network are studied under asynchronous and synchronization modes. Principle Component Analysis (PCA) is used to reduce the data dimension, and Support Vector Machine (SVM) is used to classify the synchronous and asynchronous data. We define that oscillator with stronger coupling weight (lower coupling resistance) as a leader oscillator. From the spice simulation, when OSC<sub>1</sub> and OSC<sub>2</sub> work as leader oscillator, the ONN almost always achieve synchronization; and the synchronization frequency is close to the average value of the leader oscillators. By training the emerging synchronous and asynchronous data, we can predict the synchronization status of an unknown dataset. Weight retrieval can be achieved by adjusting the slope and bias of the separation boundary.
Highlights
Recent trends in information processing place high demands on a complex dynamical system in a small area and still reduce power consumption [1,2,3,4,5]
When the coupling resistance value of one oscillator is under a very small value (< 10 kΩ), and it is much smaller than the other oscillators, we define that oscillator with stronger coupling weight as leader oscillator
The simulation result tells us that when OSC1 and OSC2 work as leader oscillator, the ONN almost always achieve synchronization and the synchronization frequency is close to the average value of the leader oscillators
Summary
Recent trends in information processing place high demands on a complex dynamical system in a small area and still reduce power consumption [1,2,3,4,5]. Considering the limitation of improvement in CMOS technology on device scaling, memory capacity, and power consumption in the near future, the CMOS based oscillatory neural network for analog or non-Boolean computing applications has aroused interest among researchers for energy-efficient computational units. In comparison with digital computing (Boolean operation) [7], the potential of using CMOS technology to perform analog computing (non-Boolean computation) remains an opportunity because of its energy efficiency [8,9]. The coupled oscillatory neural network provided the possibility of performing computations in reduced power consumption without changing the device scale.
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