The paper presents a method for forming a reservoir of a neural network LogNNet using a linear congruent pseudo-random number generator. This method made it possible to reduce the MNIST handwritten digit recognition time on the low-memory Arduino Uno board to 0.28 s for the LogNNet 784:20:10 configurations, with a classification accuracy of ~ 82%. It was found that the computations with integers gives an increase in the speed of the algorithm by more than 2 times in comparison with the algorithm using the real type when generating a chaotic time series. The developed method can be used to accelerate the calculations of edge devices in the field of “Internet of Things”, for example, for mobile medical devices, autonomous vehicle control systems and bionic suit control.
The presented compact algorithm for recognizing handwritten digits of the MNIST database, created on the LogNNet reservoir neural network, reaches the recognition accuracy of 82%. The algorithm was tested on a low-memory Arduino board with 2 Kb static RAM low-power microcontroller. The dependences of the accuracy and time of image recognition on the number of neurons in the reservoir have been investigated. The memory allocation demonstrates that the algorithm stores all the necessary information in RAM without using additional data storage, and operates with original images without preliminary processing. The simple structure of the algorithm, with appropriate training, can be adapted for wide practical application, for example, for creating mobile biosensors for early diagnosis of adverse events in medicine. The study results are important for the implementation of artificial intelligence on peripheral constrained IoT devices and for edge computing.
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