The tutorial titled “How to Build Open-Source Neuromorphic Hardware and Algorithms” will run in-person at the IEEE International Symposium on Circuits and Systems in Monterey, CA, USA.

Tutorial Overview: The brain is the perfect place to look for inspiration to develop more efficient neural networks. While the computational cost of deep learning exceeds millions of dollars to train large-scale models, our brains are somehow equipped to process an abundance of signals from our sensory periphery within a power budget of approximately 10-20 watts. The brain’s incredible efficiency can be attributed to how biological neurons encode data in the time domain as spiking action potentials.

This tutorial will take a hands-on approach to learning how to train spiking neural networks (SNNs), and designing neuromorphic accelerators that can process these models. With the advent of open-sourced neuromorphic training libraries and electronic design automation tools, we will conduct hands-on coding sessions to train SNNs, and attendees will subsequently design a lightweight neuromorphic accelerator in the SKY130 process. Participants will be equipped with practical skills that apply principles of neuroscience to deep learning and hardware acceleration in building the next generation of machine intelligence.

Jason Eshraghian is delivering an invited talk at the 2023 Neuro-Inspired Computing Elements “All Aboard the Open-Source Neuromorphic Hype Train” in San Antonio, Texas, USA in April.

The presentation session will give an overview for neuromorphic hardware developed in open-source processes, and highlight the Tiny Neuromorphic Tape-out project that will take place at the Telluride Neuromorphic Cognition and Engineering Workshop.