Today, my latest work, “Ψ-Vortex: Structure-Regularized Recurrent Learning for Latent Thermal-Coupling Inference and Verilog-A Compact Modeling of Three-Dimensional Neuromorphic Devices,” has been published in IEEE Access (IF: 4.2). The supplementary experiments document can be seen here. The Ψ-Vortex design rationale document can be seen here. The code, data, configurations, trained artifacts, and reproducibility materials for [...]
There’s a saying floating around the hardware world that sums up one of the biggest mistakes in Edge AI right now: „Don’t kill a mosquito with a cannon.“ Note: Please, never kill insects. They are advanced living beings and deserve respect. If you hurt one by mistake, apologize for it, as I do. Sometimes this [...]
The promise of this tutorial: the companion tutorial on Ψ-NN ended with a triumph: a neural network that can’t break a law of physics, and that learns how to be that way on its own. But that network had a quiet problem: it could only talk to other neural networks. This tutorial picks up exactly [...]
The promise of this tutorial: by the end you will understand, step by step, how Physics Structure-Informed Neural Network (Ψ-NN) takes an ordinary neural network and rebuilds it so that it literally cannot break a law of physics, and how it figures out how to do that all by itself. We will keep every idea [...]
Today, I want to share with you another story (after this similar one shared earlier) about bridging machine learning with circuit simulation. Recently, I succeeded in teaching neural networks to speak the language of circuits again, this time however, I solved the spectral bias problem! You can read more about this success in my research [...]
After I published my earlier blog post on Ψ-NN (Physics Structure-Informed Neural Networks), I received a very kind comment from one of the authors. In that reply, they pointed me to two earlier projects from the same research line: AsPINN (Adaptive Symmetry-Recomposition Physics-Informed Neural Networks) and AtPINN (Adaptive Transfer Learning for PINN). At first glance, [...]
Happy to announce that I’ve taken a meaningful step toward bridging the gap between AI and circuit/device modeling. It is my absolute pleasure to introduce Ψ-HDL (pronounced Psi-HDL), my Physics Structure-Informed Hardware Description Language framework. This work builds directly on the Ψ-NN (Physics structure-informed neural network) discovery framework introduced by Liu et al. (Nature Communications, [...]
Every so often, a paper comes along that feels like it bridges two worlds that have been talking past each other. Recently, I came across one of those papers: “Automatic network structure discovery of physics informed neural networks via knowledge distillation” by Ziti Liu and colleagues. It’s an ambitious piece of work that tries to [...]
Which Ψ Framework Should You Use? Ψ-NN, Ψ-HDL, Ψ-xLSTM, or Ψ-Vortex?A from-scratch tutorial on Ψ-Vortex („Psi-Vortex“)Ψ-Vortex: Structure-Regularized Recurrent Learning for Latent Thermal-Coupling Inference and Verilog-A Compact Modeling of Three-Dimensional Neuromorphic DevicesToday I Submitted My First MSCA-PF Research ProposalXiaomi Portable Electric Air Compressor 2 Review: Real-World Test on a Dacia Duster Mat Edition
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