arXiv Machine Learning
1d ago

Circuit realization and hardware linearization of monotone operator equilibrium networks

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.

By Thomas Chaffey
arXiv AI
Jun 29

Derivation of effective gradient flow equations and dynamical truncation of training data in Deep Learning

arXiv:2501. 07400v2 Announce Type: replace-cross Abstract: We derive explicit equations governing the cumulative biases and weights in Deep Learning with ReLU activation function, based on gradient descent for the Euclidean loss in the input layer, and under the assumption that the weights are, in a precise sense, adapted to the coordinate system distinguished by the activations.

By Thomas Chen