Memristors drive digital reservoir neural network

Memristors drive digital reservoir neural network

Technology News |
By Nick Flaherty

A Chinese AR technology company has developed a memristor-based digital reservoir (DR) computing system for real-time and energy-efficient signal processing.

The technique developed by WiMi Hologram Cloud combines a reservoir of memristor circuits with DR technology to simulate the dynamic behaviour of neurons and process time-series data for more efficient signal processing.

DR technique is a feedback-based neural network algorithm that takes the input signal as the initial state and projects it into a high-dimensional space for processing in a continuous iterative process.

The reservoir pool consists of a set of randomly connected neurons connected randomly and fixed. In the reservoir pool, the input signals are mapped to the states of the neurons, and then after a certain period, the output signals are extracted. The states of the reservoir pool can be thought of as points in a high-dimensional space and, therefore, can be analyzed and processed using a dynamical systems approach.

The technique is increasingly used in in speech recognition, image processing and the Internet of Things.

Unlike traditional digital reservoir technology, WiMi’s system can be designed using analog circuitry to implement reservoir technology’s randomly connected neuron structure. The memristor enables high-speed, low-power analog computation and fast reset and initialization operations, ideally suited for reservoir pool construction.

In this system, the input signal of the reservoir can be analog or digital. And the output signal of the reservoir can be used for tasks such as classification, regression, and time series prediction. In addition, the system can use digital signal processing techniques, such as fast Fourier transform and wavelet transform, to further process the output signal of the reservoir.

The core principle of the system is to process the input signal through the reservoir and output the processed result by constructing a large-scale, high-dimensional, random reservoir and using the nonlinear dynamic property of the memristor.

The reservoir is a dynamic system with memory that can transform and delay the input signal nonlinearly, thus improving the processing efficiency and accuracy of the signal. The input signal is pre-processed and fed into the reservoir in this system. The reservoir processes the signal through the nonlinear dynamic response of the memristor and outputs the processed results to a subsequent neural network or classifier for further processing.

This allows the system can process many input signals quickly and efficiently and the DR has memory, which can delay and transform the input signal, improving the stability and accuracy of the system.

The system can be adapted to different application scenarios by increasing the number of memristors and adjusting the structure of the reservoir, which has good scalability.


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