arXiv Machine Learning
Circuit realization and hardware linearization of monotone operator equilibrium networks
Read the original on arXiv Machine Learning →The paper demonstrates that a resistor‑diode network’s port behavior solves a ReLU monotone operator equilibrium network, effectively realizing a neural network in analog hardware. It introduces hardware linearization to compute gradients directly in the circuit, enabling in‑hardware training demonstrated via device‑level simulation. The study also extends to cascaded networks for feedforward architectures and shows how different nonlinear elements yield distinct activation functions, including a novel diode ReLU from a non‑ideal diode model.
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