# Hybrid Synaptic Structure for Spiking Neural Network Realization

arXiv: https://arxiv.org/abs/2311.07787 · HTML: https://mlsanity.com/paper/2311.07787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2023.

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

- [Self-training superconducting neuromorphic circuits using reinforcement learning rules](https://mlsanity.com/paper/2404.18774/) (2024)

## Abstract (arXiv)

Neural networks and neuromorphic computing play pivotal roles in deep learning and machine vision. Due to their dissipative nature and inherent limitations, traditional semiconductor-based circuits face challenges in realizing ultra-fast and low-power neural networks. However, the spiking behavior characteristic of single flux quantum (SFQ) circuits positions them as promising candidates for spiking neural networks (SNNs). Our previous work showcased a JJ-Soma design capable of operating at tens of gigahertz while consuming only a fraction of the power compared to traditional circuits, as documented in [1]. This paper introduces a compact SFQ-based synapse design that applies positive and negative weighted inputs to the JJ-Soma. Using an RSFQ synapse empowers us to replicate the functionality of a biological neuron, a crucial step in realizing a complete SNN. The JJ-Synapse can operate at ultra-high frequencies, exhibits orders of magnitude lower power consumption than CMOS counterparts, and can be conveniently fabricated using commercial Nb processes. Furthermore, the network's flexibility enables modifications by incorporating cryo-CMOS circuits for weight value adjustments. In our endeavor, we have successfully designed, fabricated, and partially tested the JJ-Synapse within our cryocooler system. Integration with the JJ-Soma further facilitates the realization of a high-speed inference SNN.