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NEUROMORPHIC COMPUTING GROUP


BRAIN-INSPIRED SYSTEMS AT UC SANTA CRUZ

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BRAIN-INSPIRED ALGORITHMS, ARCHITECTURES AND CIRCUITS

Generated by Stable Diffusion Text-to-Image Model

The brain is the perfect place to look for inspiration to build more efficient
computers. Our goal in the UCSC Neuromorphic Computing Group led by Assistant
Prof. Jason Eshraghian is to understand the computational principles that
underpin the brain, and use them to engineer more efficient systems that can
adapt to ever-changing environments. We develop algorithms that can learn, and
low-power architectures and circuits that harness exotic device technologies.
Our work sits at the intersection of neuroscience, deep learning, and VLSI
design.

We also maintain snnTorch, a deep learning Python library that enables
gradient-based optimization of spiking neural network and has been downloaded
over 100,000 times.





COLLABORATIONS AND APPLICATIONS

Our research is actively used across domains in both research and applied
settings. Cardiologists apply our neuromorphic vision algorithms to assess the
risk of heart failure. Neurologists have successfully used spiking neural
networks to forecast the early onset of seizures. Outside of the hospital, the
same models have been used to track space junk orbiting the Earth to avoid
high-risk missions, and for forecasting power loads from renewable energy
sources in rural areas.

We’re open to collaborations and sponsorship opportunities. Feel free to get in
touch at jeshragh@ucsc.edu.

Interested in joining us? Check out current openings here

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