A new family of bioSFQ logic/memory cells

TL;DR

This paper introduces bioSFQ, a new family of superconductor logic/memory cells that encode data using quasi-analog currents and magnetic flux, aiming to efficiently implement neuromorphic computing and deep learning algorithms.

Contribution

The paper proposes and demonstrates a novel bioSFQ family of logic/memory cells that organize existing superconductor components for neuromorphic applications, with initial proof-of-concept tests.

Findings

Successful design and fabrication of basic bioSFQ cells

Demonstration of unipolar and bipolar multipliers

Proof of concept for neuromorphic superconductor logic

Abstract

Superconductor electronics (SCE) is competing to become a platform for efficient implementations of neuromorphic computing and deep learning algorithms (DLAs) with projects mostly concentrating on searching for gates that would better mimic behavior of real neurons. In contrast, we believe that most of the required components have already been demonstrated during the long history of SCE, whereas the missing part is how to organize these components to efficiently implement DLAs. We propose a family of logic/memory cells in which stored multi-bit data are encoded by quasi-analog currents or magnetic flux in superconductor loops while transmitted data are encoded as the rate of SFQ pulses. We designed, fabricated, and tested some of the basic cells to demonstrate a proof of concept, e.g., a unipolar and bipolar multipliers based on Josephson junction comparators. We coined the term bioSFQ to clearly connote close but distinguishable relations between the conventional SFQ electronics and its new neuromorphic paradigm.