Simulation study on Conservative Join (C-join) in Skyrmion Brownian circuit
H. Imanishi, E. Tamura, S. Miki, R. Ishikawa, H. Nomura, M. Goto, and, Y. Suzuki

TL;DR
This paper proposes a novel C-join circuit for skyrmion-based Brownian computing, demonstrating its synchronization capability and application in a crossing-free half-adder architecture through particle simulations.
Contribution
The study introduces a decomposed C-join circuit, validated via simulations, enabling synchronization of skyrmion signals and facilitating skyrmion-based computing architectures.
Findings
C-join synchronizes skyrmion signals within 6.8μs at 99.9% success rate
Decomposition into Join and Fork circuits is effective
Constructed a crossing-free half-adder using C-join circuits
Abstract
Magnetic skyrmions, which exhibit Brownian motion in solid-state systems, are promising candidates as signal carriers for Brownian computing. However, successfully implementing such systems requires two critical components: a Hub to connect multiple wires and a C-join to synchronize the skyrmion signal carriers. While the former has been successfully addressed, the latter remains a significant challenge. In this study, we propose a novel solution by decomposing the C-join into two sub-circuits, the Join and Fork, and validate their functionality using a particle simulation approach. Our results demonstrate that the C-join can effectively synchronize skyrmion signals within 6.8{\mu}s with a 99.9% success rate at low temperatures. Additionally, we construct the Half-adder in a crossing-free architecture utilizing the C-join circuits. These findings pave the way for the realization of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEngineering and Test Systems · Advanced Algorithms and Applications · Advanced Decision-Making Techniques
