Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11576
Title: Hybrid synaptic structure for spiking neural network realization
Authors: Razmkhah, S.
Karamuftuoglu, M. A.
Bozbey, A.
Keywords: artificial synapse
spiking neural network
single flux quantum
superconductor electronics
Flux
circuits
Publisher: IOP Publishing Ltd
Abstract: Neural networks and neuromorphic computing represent fundamental paradigms as alternative approaches to Von-Neumann-based implementations, advancing in the applications of deep learning and machine vision. Nonetheless, conventional semiconductor circuits encounter challenges in achieving ultra-fast processing speed and low power consumption due to their dissipative properties. Conversely, single flux quantum circuits exhibit inherent spiking behavior, showcasing their characteristics as a promising candidate for spiking neural networks (SNNs). In this work, we present a compact hybrid synapse circuit to mimic the biological interconnect functionality, enabling the weighting operations for excitatory and inhibitory impulses. Additionally, the proposed structure facilitates input accumulation, which is performed before the activation function. In the experiments, our synaptic structure interfaces with a soma circuit fabricated using a commercial Nb process, underscoring its compatibility and supporting its potential for integration into efficient neural network architectures. The weight value on the synapse is configurable by utilizing cryo-CMOS circuits, providing adaptability to the inference networks. We've successfully designed, fabricated, and partially tested the JJ-Synapse within our cryocooler system, enabling high-speed inference implementation for SNNs.
URI: https://doi.org/10.1088/1361-6668/ad44e3
https://hdl.handle.net/20.500.11851/11576
ISSN: 0953-2048
1361-6668
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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