A Large-Scale Simulation Method for Neuromorphic Circuits
arxiv(2024)
摘要
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.
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