A new family of bioSFQ logic/memory cells

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.

6 pages, 6 figures, 29 references. Presented at 15-th European Conference on Applied Superconductivity, September 5-9, 2021, presentation #291

References in corpus (4)

• Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
• Synchronization dynamics on the picosecond timescale in coupled Josephson junction neurons
• Fan-out and Fan-in properties of superconducting neuromorphic circuits
• SFQ bias for SFQ digital circuits

Cited by in corpus (4)

• BioSFQ circuit family for neuromorphic computing: Bridging digital and analog domains of superconductor technologies
• Scalability of Superconductor Electronics: Limitations Imposed by AC Clock and Flux Bias Transformers
• Self- and Mutual Inductance of NbN and Bilayer NbN/Nb Inductors in Planarized Fabrication Process With Nb Ground Planes
• Digital-analog concept for superconducting perceptron-like neural networks