Energy-Efficient Cryogenic Neuromorphic Network with Superconducting Memristor

TL;DR

This paper presents a cryogenic neuromorphic network using superconducting memristors that demonstrates low power consumption and effective real-time decision-making in dynamic tasks, advancing energy-efficient computing.

Contribution

It introduces a fully integrated superconducting memristor-based neuromorphic framework validated on a control task, highlighting its potential for ultra low power and scalable systems.

Findings

Achieved 5965 timesteps average fitness in cart pole task

Demonstrated 23 distinct spiking rates for efficient encoding

40% of test episodes reached the target fitness of 15,000 timesteps

Abstract

Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines superconducting memristor(SM) based spiking neurons and synapse topologies to achieve a low power neuromorphic network with non volatile synaptic strength.This neurosynaptic framework is validated by implementing the cart pole control task, a dynamic decision making problem requiring real time computation.Through detailed simulations, we demonstrate the network's ability to execute this task with an average fitness of 5965 timesteps across 1000 randomized test episodes, with 40 percent achieving the target fitness of 15,000 timesteps (0.02s per timestep).The system achieves 23 distinct spiking rates across neurons, ensuring efficient information encoding.Our findings establish the potential of SM based cryogenic neuromorphic systems to address the energy and scalability limitations of traditional computing, paving the way for biologically inspired, ultra low power computational frameworks.