Neuromorphic Functional Modules of Spiking Neural Network
Authors:   St. Petersburg State Electrotechnical University LETI named after V. I. Ulyanov (Lenin), St. Petersburg, 197376, Russian Federation, Ryndin E.A. , Andreeva N.V. ,   St. Petersburg State Electrotechnical University LETI named after V. I. Ulyanov (Lenin), St. Petersburg, 197376, Russian Federation, Luchinin V.V. ,   St. Petersburg State Electrotechnical University LETI named after V. I. Ulyanov (Lenin), St. Petersburg, 197376, Russian Federation, Goncharov K.S. ,   St. Petersburg State Electrotechnical University LETI named after V. I. Ulyanov (Lenin), St. Petersburg, 197376, Russian Federation, Raiimzhonov V.S. ,   St. Petersburg State Electrotechnical University LETI named after V. I. Ulyanov (Lenin), St. Petersburg, 197376, Russian Federation
Published: December 2021

In the current era, design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks effectively solve a wide range of common for artificial intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of object behavior. Biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to heavy parallel and asynchronous data processing. In this paper, we present the circuit design of main functional blocks (neurons and synapses) intended for hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timing spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approach to emulate the synaptic behavior in hardware implemented spiking neural networks is to use non-volatile memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structure to mimic a synaptic plasticity, pre- and postsynaptic neurons which could be used for developing circuit design of spiking neural network architectures with different training algorithms including spike-timing dependent plasticity learning rule. Two different circuits of electronic synapse were developed. The first one is an analog synapse with photoresistive optocoupler used to ensure the tunable conductivity for synaptic plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without digital-to-analog converter andphotoresistive optocoupler). The results of the prototyping of developed circuits for electronic analogues of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for ASIC design of spiking neural networks based on CMOS (complementary metal oxide semiconductor) design technology.