Audience: Staff

Presenter: Chen-Hsiang Yeang, Associate Research Fellow, Institute of Statistical Science of Academia Sinica Description: Computational models are roughly categorized into two types: describing the patterns of the phenomenon or data (description-driven models) and explaining the phenomenon or data with simpler, comprehensible rules (explanation-driven models). When building a model, the choice of the mixture ingredients of…

Presenter: Jason Yik, PhD Candidate, Harvard SEAS Description: Recent research on neuromorphic accelerators has investigated their efficiency and performance benefits for machine learning (ML) inference at the edge. This talk will focus on the performance implications of the fully-on-chip, manycore-distributed memory architecture used by current neuromorphic accelerators. In conventional architectures, the roofline model is a…

Presenter: Ben Keitz, Associate Professor, University of Texas at Austin Description: Qualities exhibited by living systems, including self-regulation, self-healing, morphology control, and environmental responsiveness, are highly attractive for sensing and computing applications. However, it has been challenging to develop robust and programmable interfaces between living systems and electronic components. Addressing this challenge, our lab employs…