A Cryogenic Artificial Synapse based on Superconducting Memristor
Md Mazharul Islam, Shamiul Alam, Md Rahatul Islam Udoy, Md Shafayat Hossain, Ahmedullah Aziz
Abstract
Spiking neural network (SNN) has emerged as the most biologically accurate approach for information encoding in neuromorphic computing. Cryogenic neuromorphic hardware, which offers exceptional energy efficiency and speed, has recently gained enormous attention among the neuromorphic community. An approach to build such neuromorphic hardware is to use a conductance asymmetric superconducting quantum interference device (CA-SQUID) that has non-volatile and variation- robust dual-resistive behavior and thereby, is referred to as a superconducting memristor (SM). Here, we utilize this unique device to design an SM-based artificial synapse topology for neuromorphic applications. The proposed synapse structure, combined with an SM-based neuron, demonstrates neurosynaptic behavior with enhanced reconfigurability. Our design features eight different non-volatile levels of synaptic strength, utilizing combinations of distinct resistance levels of three SMs, exhibiting an estimated programming power of 8.5 pW. This weight storage feature enables better reconfigurability compared to the existing superconducting synapse structures that utilized fixed resistors and inductors. Additionally, this synapse can be further fine-tuned to dynamically access a wide range of synaptic strengths by using an external bias current. Our study provides valuable insights into the system-level integration of the neuron-synaptic architecture.