“A new open source code library, snnTorch, has surpassed 100,000 downloads and is used in a wide variety of projects — from NASA satellite tracking efforts to optimizing chips for AI.”
See the full article here.
Category Archives: Media
Prof. Jason Eshraghian Presents Invited Talk at FOSSi Latch-Up 2023 (Santa Barbara, CA, USA)
Jason Eshraghian gave an invited talk at the 2023 Free and Open Source Silicon (FOSSi) Latch-Up Conference “Open Source Brain-Inspired Neuromorphic Software and Hardware” at UC Santa Barbara, CA, USA.
The presentation session will give an overview for how open source tooling has been used to propose and implement neuromorphic solutions and applications. The presentation will highlight the Tiny Neuromorphic Tape-out project that will take place at the Telluride Neuromorphic Cognition and Engineering Workshop.
The recording is available on YouTube.
Graphcore Collaboration has snnTorch running on Intelligent Processing Units
We now have snnTorch accelerated on Graphcore’s Intelligent Processing Units (IPUs), co-developed by Vincent Sun, Jason Eshraghian, along with assistance from Graphcore.
See this link for a tutorial to train SNNs on IPUs.
snnTorch at Facebook’s PyTorch 2021 Dev Day
We are presenting snnTorch at the PyTorch Dev Day on 1/2 Dec 2021! We will demonstrate some of the current capabilities of SNNs, and chat about our future plans for expanding the snnTorch ecosystem.
snnTorch at PyTorch Developer Day
Check out the line up of speakers here.
Prof. Jason Eshraghian Presents “Deep Learning with snnTorch” Tutorial at ICONS 2021
The tutorial Jason gave at the International Conference on Neuromorphic Systems on training spiking neural networks using modern deep learning frameworks is online – check it out at the link here.
IEEE ECCTD 2020 Keynote Address: CMOS-Memristor Nanoelectronics for Neuromorphic Computing
Professor Sung-Mo Kang and Jason Eshraghian delivered the keynote address for the IEEE European Conference on Circuit Theory and Design titled “CMOS-Memristor Nanoelectronics for Neuromorphic Computing”.
The talk takes a journey from the early stages of CMOS design to current memristor nanoelectronics with critical views for device threading, interconnect and technology for achieving multidimensional design goals: reliability, throughput performance, energy consumption and manufacturing costs.
These principles are applied to neuromorphic systems for brain-inspired computation. The powerful capabilities of these neuromorphic processors can be applied to a plethora of real-world challenges, from data-driven healthcare, to neurostimulation, and in AI-generated artwork, as we make a profound shift away from the sequential processing of Von Neumann machines towards parallel, interconnected neural-inspired structures.
Watch the recording here: http://ecctd2020.eu/node/22
Editorial in Nature Machine Intelligence
One neural network is good for recognising patterns, but two are great for creating them. Nature Machine Intelligence did a neat write-up about our legal research on determining who owns artificially generated patterns, images, videos, and artwork.
Picture produced with generative AI art tool, artbreeder, by Jacob Huth
Read more here.