Jason Eshraghian
jeshraghian
Assistant Professor at the University of California, Santa Cruz. Leading the Neuromorphic Research Computing Group.
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Top Repositories
Deep and online learning with spiking neural networks in Python
Quantization-aware training with spiking neural networks
Notebooks for Hardware-Aware Training of Spiking Neural Networks. Open-Source Neuromorphic Circuit Design Tutorial at ESSCIRC 2023.
Threshold annealing in binarized spiking neural networks
Notebooks for the 2023 Tutorial: "How to Build Open Source Neuromorphic Hardware and Algorithms"
Repositories
32Deep and online learning with spiking neural networks in Python
Notebooks for Hardware-Aware Training of Spiking Neural Networks. Open-Source Neuromorphic Circuit Design Tutorial at ESSCIRC 2023.
Quantization-aware training with spiking neural networks
Threshold annealing in binarized spiking neural networks
No description provided.
No description provided.
Syllabus for CMPM 118 in the Neurimorphic Computing Group (NCG)
A Silicon Hodgkin-Huxley Neuron on TinyTapeout
No description provided.
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
Notebooks for the 2023 Tutorial: "How to Build Open Source Neuromorphic Hardware and Algorithms"
A Simulation Framework for Memristive Deep Learning Systems
Notebooks for Memristec Summer School (Dresden, Germany)
my neuromorphic submission to tinytapeout
No description provided.
Test demo for tt08.
Implementation for MatMul-free LM.
Notebooks and code for Spiking Neural Network Hands-on session at the INVICTA Spring School 2024, Porto, Portugal.
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
No description provided.
Brevitas: quantization-aware training in PyTorch
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Publicly available event datasets and transforms.
A conda-smithy repository for snntorch.
A community dedicated to supporting tools for technical and scientific communication and interactive computing
A place to submit conda recipes before they become fully fledged conda-forge feedstocks
No description provided.
gradient descent using a memristive integrate-and-fire neuron model
Tensors and Dynamic neural networks in Python with strong GPU acceleration
No description provided.