Abstract
The smart devices in Internet of Things (IoT) need more effective data storage opportunities, as well as support for Artificial Intelligence (AI) methods such as neural networks (NNs). This study presents a design of new associative memory in the form of impulsive Hopfield network based on leaky integrated-and-fire (LIF) RC oscillators with frequency control and hybrid analog–digital coding. Two variants of the network schemes have been developed, where spiking frequencies of oscillators are controlled either by supply currents or by variable resistances. The principle of operation of impulsive networks based on these schemes is presented and the recognition dynamics using simple two-dimensional images in gray gradation as an example is analyzed. A fast digital recognition method is proposed that uses the thresholds of zero crossing of output voltages of neurons. The time scale of this method is compared with the execution time of some network algorithms on IoT devices for moderate data amounts. The proposed Hopfield algorithm uses rate coding to expand the capabilities of neuromorphic engineering, including the design of new hardware circuits of IoT.
Highlights
The number of real-world Internet of Things (IoT) deployments is continuously and steadily increasing, but the capabilities of single IoT devices cannot yet be exploited for the purpose of artificial intelligence (AI)
In recent decades spike neural networks (SNNs) have been intensively developed in this direction [3,4], they are still inferior to classical NNs using threshold adders in speed and accuracy of doing tasks in most application areas
We developed a design of new associative memory, which is an SNN of oscillator type and has Hopfield architecture and algorithm of the energy function
Summary
The number of real-world Internet of Things (IoT) deployments is continuously and steadily increasing, but the capabilities of single IoT devices cannot yet be exploited for the purpose of artificial intelligence (AI). The main reason is computation complexity and energy consumption, which are the constraining requirements for the development and implementation of truly intelligent IoT devices with AI [1,2]. A solution to address this problem could be to use the chips for IoT devices based on NNs with low energy consumption and simplified computing base. The obvious proximity to the operation of real biological neurons combined with greater variability in learning and coding make SNNs more promising than traditional NNs of firstand second-generation. There are two main coding methods in SNNs: temporal coding and firing rate coding [5,6,7,8,9,10,11]
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