T3: Open-source neuromorphic circuit design
Overview, trends, and opportunities
Half day, 10:00
As a bio-inspired alternative to conventional machine-learning accelerators, neuromorphic circuits outline promising energy savings for extreme-edge scenarios. While still being considered as an emerging approach, neuromorphic chip design is now being included in worldwide research roadmaps: the community is growing fast and is currently catalyzed by the development of open-source design tools and platforms. In this tutorial, we will survey the diversity of the open-source neuromorphic chip design landscape, from digital and mixed-signal small-scale proofs-of-concept to large-scale platforms. We will also provide a hands-on overview of the associated design challenges and guidelines, from which we will extract upcoming trends and promising use cases.
Charlotte Frenkel (Delft University of Technology, NL)
Charlotte Frenkel is an Assistant Professor at Delft University of Technology, The Netherlands. She received her Ph.D. from UC Louvain, Belgium, in 2020 and was a post-doctoral researcher at UZH and ETH Zürich, Switzerland. Her research aims at bridging the bottom-up (neuroscience-driven) and top-down (engineering-driven) neuromorphic design approaches, with a focus on digital spiking neural network processor design, embedded machine learning, and brain-inspired on-device learning. She received a best paper award at ISCAS 2020, and her Ph.D. thesis was awarded the FNRS / Nokia Bell Scientific Award 2021 and the FNRS / IBM Innovation Award 2021. She is the chair of the tinyML initiative on neuromorphic engineering, is a TPC member of ESSCIRC and DATE, and serves as an associate editor for the IEEE Trans. on Biomedical Circuits and Systems.