Research & Papers

On Port-Hamiltonian Formulation of HystereticEnergy Storage Elements: The Backlash Case

A new port-Hamiltonian framework could lead to more efficient neuromorphic chips and AI accelerators.

Deep Dive

A team of researchers from the University of Groningen, led by Jurrien Keulen, Bayu Jayawardhana, and Arjan van der Schaft, has published a significant theoretical paper on arXiv. Their work, 'On Port-Hamiltonian Formulation of Hysteretic Energy Storage Elements: The Backlash Case', introduces a rigorous mathematical framework for modeling components with memory-like, hysteretic behavior. Using port-Hamiltonian system theory—a powerful method for describing energy flow in physical systems—they formally derive a family of 'storage functions' that quantify how energy is stored and dissipated in these non-linear elements, such as hysteretic inductors.

This foundational research is not just an abstract exercise. It provides the precise mathematical tools needed to design and analyze circuits that are inherently more energy-efficient. By accurately modeling hysteresis, engineers can better understand and minimize energy losses. The paper demonstrates this applicability by showing how to integrate a hysteretic inductor into classic RLC circuits. This work is a critical step toward the practical realization of neuromorphic computing systems, which aim to mimic the brain's efficient, event-driven processing and could form the basis of a new generation of low-power AI accelerators.

Key Points
  • Develops a port-Hamiltonian framework to model hysteretic (memory-retaining) energy storage elements, formalizing their energy dynamics.
  • Explicitly derives the 'available storage' and 'required supply' functions, providing a rigorous way to analyze energy efficiency and passivity.
  • Demonstrates practical application by integrating a hysteretic inductor into RLC circuits, a foundational step for neuromorphic hardware design.

Why It Matters

Provides the mathematical backbone for designing energy-efficient, brain-inspired computer chips crucial for sustainable AI development.