2024/04/09 by Amir Shahhosseini, Shahhosseini, Amir, Thomas Chaffey +3 · 4 citations
Engineering · Physics and Astronomy · #Advanced Memory and Neural Computing #FOS: Electrical engineering #Ferroelectric and Negative Capacitance Devices #Semiconductor Quantum Structures and Devices #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2404.06255
openalex publication_date 2024/04/09 · openalex created_date 2024/04/12 · openalex updated_date 2026/07/28
Splitting algorithms are well-established in convex optimization and are designed to solve large-scale problems. Using such algorithms to simulate the behavior of nonlinear circuit networks provides scalable methods for the simulation and design of neuromorphic systems. For circuits made of linear capacitors and inductors with nonlinear resistive elements, we propose a splitting that breaks the network into its LTI lossless component and its static resistive component. This splitting has both physical and algorithmic advantages and allows for separate calculations in the time domain and in the frequency domain. To demonstrate the scalability of this approach, a network made from one hundred neurons modeled by the well-known FitzHugh-Nagumo circuit with all-to-all diffusive coupling is simulated.