Combining Neuroscience and Artificial Intelligence for Efficient Processing of Data

Monday - November 27 2023, 19:13 UTC - 12 months ago

UC Santa Cruz's Jason Eshraghian has created a Python library called “snnTorch”, which has surpassed 100,000 downloads and is used for a variety of projects, such as NASA satellite tracking efforts and optimizing chips for AI. This code combines neuroscience with artificial intelligence to create spiking neural networks, and is accompanied by a paper that provides an honest and comprehensive guide to learning about and using snnTorch for new students and researchers.

Four years ago, UC Santa Cruz's Jason Eshraghian developed a Python library that combines neuroscience with artificial intelligence to create spiking neural networks, a machine learning method that takes inspiration from the brain's ability to efficiently process data. Now, his open source code library, called "snnTorch," has surpassed 100,000 downloads and is used in a wide variety of projects, from NASA satellite tracking efforts to semiconductor companies optimizing chips for AI.Apaper published in the journal Proceedings of the IEEE documents the coding library but also is intended to be a candid educational resource for students and any other programmers interested in learning about brain-inspired AI.

"It's exciting because it shows people are interested in the brain, and that people have identified that neural networks are really inefficient compared to the brain," said Eshraghian, an assistant professor of electrical and computer engineering. "People are concerned about the environmental impact [of the costly power demands] of neural networks and large language models, and so this is a very plausible direction forward." .

Building snnTorch .

Spiking neural networks emulate the brain and biological systems to process information more efficiently. The brain's neurons are at rest until there is a piece of information for them to process, which causes their activity to spike. Similarly, a spiking neural network only begins processing data when there is an input into the system, rather than constantly processing data like traditional neural networks.

"We want to take all the benefits of the brain and its power efficiency and smush them into the functionality of artificial intelligence—so taking the best of both worlds," Eshraghian said.

Eshraghian began building the code for a spiking neural network in Python as a passion project during the pandemic, somewhat as a method to teach himself the coding language Python. A chip designer by training, he became interested in learning to code when considering that computing chips could be optimized for power efficiency by co-designing the software and the hardware to ensure they best complement each other.

Now, snnTorch is being used by thousands of programmers around the world on a variety of projects, supporting everything from NASA's satellite tracking projects to major chip designers such as Graphcore.

While building the Python library, Eshraghian created code documentation and educational materials, which came naturally to him in the process of teaching himself the coding language. The documents, tutorials, and interactive coding notebooks he made later exploded in the community and became the first point of entry for many people learning about the topics of neuromorphic engineering and spiking neural networks, which he sees as one of the major reasons that his library became so popular.

An honest resource .

Knowing that these educational materials could be very valuable to the growing community of computer scientists and beyond who were interested in the field, Eshraghian began compiling his extensive documentation into a paper.

The paper acts as a companion resource to the code, which unlike typical research papers focused solely on the successes of the projects, instead offering an honest and comprehensive guide to learning about and using snnTorch.

"This was an opportunity to document the entire library, and to make a resource that is useful to other researchers, professors, students, and people just coming into the field," Eshraghian said.