# Cryogenic Neuromorphic Hardware

arXiv: https://arxiv.org/abs/2204.07503 · HTML: https://mlsanity.com/paper/2204.07503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 110 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Spiking Neural Networks and Online Learning: An Overview and Perspectives](https://mlsanity.com/paper/1908.08019/) (2019)
- [Superconducting optoelectronic circuits for neuromorphic computing](https://mlsanity.com/paper/1610.00053/) (2016)
- [A superconducting nanowire spiking element for neural networks](https://mlsanity.com/paper/2007.15101/) (2020)
- [Superconducting Optoelectronic Neurons III: Synaptic Plasticity](https://mlsanity.com/paper/1805.01937/) (2018)
- [A Power Efficient Artificial Neuron Using Superconducting Nanowires](https://mlsanity.com/paper/1907.00263/) (2019)

## Built on by (most cited first, 1 of 1)

- [Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks](https://mlsanity.com/paper/2311.05820/) (2023)

## Abstract (arXiv)

The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. To mitigate this, Neuromorphic computing has drawn immense attention due to its excellent capability for data processing with very low power consumption. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Furthermore, design complexity and process variation hinder the large-scale implementation of current neuromorphic platforms. Recently, the concept of implementing neuromorphic computing systems in cryogenic temperature has garnered intense interest thanks to their excellent speed and power metric. Several cryogenic devices can be engineered to work as neuromorphic primitives with ultra-low demand for power. Here we comprehensively review the cryogenic neuromorphic hardware. We classify the existing cryogenic neuromorphic hardware into several hierarchical categories and sketch a comparative analysis based on key performance metrics. Our analysis concisely describes the operation of the associated circuit topology and outlines the advantages and challenges encountered by the state-of-the-art technology platforms. Finally, we provide insights to circumvent these challenges for the future progression of research.