Yigit Demirag

I'm a Research Scientist at Google in Zürich. I focus on efficient and exotic neural computing hardware.

I did a few internships in the past, including a student researcher role at Google, working on bio-inspired quantized training with Blake Richards at MILA, modeling memristive memories at Advanced Technology Development Group of Samsung Electronics, implementing backpropagation on analog crossbar arrays at EPFL and SIMD optimization of monte carlo simulations at CERN.

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Selected Publications

Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems

Network of biologically plasuible neuron models can solve motor tasks through heterogeneity

DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delays

Overcoming phase-change material non-idealities by meta-learning for adaptation on the edge

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing

Biologically-inspired training of spiking recurrent neural networks with neuromorphic hardware

PCM-Trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials

Online training of spiking recurrent neural networks with phase-change memory synapses

Analog weight updates with compliance current modulation of binary ReRAMs for on-chip learning

Modeling electrical resistance drift with ultrafast saturation of OTS selectors

Multiphysics modeling of GST-based synaptic devices for brain inspired computing

Open Source

Gradients without Backpropagation

JAX implementation of the Gradients without Backpropagation paper, which gets rid of backpropagation of errors and estimate unbiased gradient of loss function during single inference pass via perturbed Jacobian-vector product.

e-prop local learning rule for training recurrent networks

JAX implementation of e-prop algorithm. It is written to be simple, clean and fast. I replicated the pattern generation task as described in the paper.

Dynap-SE1 Neuromorphic Processor Simulator

A minimal and interpretable Brian2 based DYNAP neuromorphic processor simulator for educational purposes.

Spiking Neural Network Simulator for TPU

A simple Colab notebook for the training of recurrent spiking neural networks on TPU or multi-GPU settings. On a single A100 GPU, it can train a small size network of 512 LIF neurons via SHD dataset under <50 sec.

Physical model of HfO2/TiOx Bilayer ReRAM memory cell

Compact model simulation of ReRAM cell. The model simulates IV characteristics of the cell, incorporating gradual SET and abrupt RESET processes. It considers filament geometry as a combination of plug and disc, and employs the Schottky barrier to represent the metal/oxide interface’s high work function, which depends on the oxide’s defect concentration.

2023 🪴