Scalable HW-Aware Training for Analog In-Memory Computing

Optimization solver backend for analog optical computer

Unsorted Playground for Machine Learning, Reinforcement Learning and other AI Experiments

A Recursive Ontological Framework for Cognitive Design, Neurodivergence Modeling, AI Co-Development, and "Structural AI"

Optical neural computation on a commodity smartphone: OLED screen + mirror + front camera as an analog matrix engine. 101 experiments, bilingual docs, and 6 Zenodo papers.

Applying IBM's Analog Hardware Acceleration Kit for in-memory computing design — neural network training on analog devices

IHP26a TinyTapeout implementation of a RISC-V CPU with an integrated SRAM-based compute-in-memory (CIM) accelerator for performing efficient analog matrix multiplications.

Exploring super-convergence techniques for analog in-memory computing — accelerating neural network training

Analog Computing for Signal Processing and Communications - Part II: Toward Gigantic MIMO Beamforming

Analog Computing for Signal Processing and Communications - Part I: Computing With Microwave Networks

Differentiable Analog Neural Network Simulation — from PyTorch to SPICE. 77.58% Fashion-MNIST, 42/42 SPICE match, scaling law R²=0.9385

MIMO Systems Aided by Microwave Linear Analog Computers: Capacity-Achieving Architectures with Reduced Circuit Complexity

Analog computer module to transform polar- into cartesian-coordinates

A programming language for wave-based phononic processors. Computation as resonance, memory as sustained oscillation, control flow as phase gating—designed from acoustic physics, not transistor assumptions.

Ripple filter for the power supply of a Analog Computer.

Hardware-agnostic AI compiler suite. Compile GGUF, ONNX, PyTorch, and SafeTensors models onto FPGAs, analog circuits, MCU swarms, photonic MZI meshes, neuromorphic chips, and CIM accelerators — not GPUs. Includes SiL emulator, real-time dashboard, federated learning, and carbon-aware compilation.

A math extension module providing the absolute and square-root function of the input

A bio-inspired analog compute substrate that learns on-chip — online, local, and forward-only. Weights live as analog charge on capacitors (compute-in-memory); an unsupervised forward-only front (SCFF) does ~80% of the work, a small gradient-descent namer the rest. A chip-design bet, not an ML model.

This repository showcases projects carried out in the "Computer Tools for Electrical Engineers" course, focusing on MATLAB and PSPICE applications in Electrical Engineering.

Kernel experimental de computación por sincronización de fase, geometría toroidal y dinámica armónica discreta en base 60.**