Quantized-state approximation of nonlinear dynamical systems for hardware-efficient implementation

Abstract

Nonlinear dynamical systems such as biological systems require substantial resources for digital circuit implementation due to their nonlinearity. This study approximates these systems by converting to the quantized-state system (QSS) solved using the forward Euler method and efficiently implements them on a field-programmable gate array (FPGA). Focusing on coupled nonlinear oscillators that mimic neural circuits, we evaluate the benefits and limitations of the QSS in terms of hardware resource usage and accuracy compared to the original system solved using the forward Euler method. The results provide valuable insights for the miniaturization and energy efficiency of neuromorphic hardware.