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Here’s the abstract for today’s virtual presentation.
“IEEE CS SD 2023 Invited Seminar Series: Lecture 2
#Neuromorphic #Computing promises orders of magnitude improvement in energy efficiency compared to the traditional von Neumann computing paradigm. The goal is to develop an #adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating #brain functionality which can be realized through innovation in different abstraction layers including material, device, circuit, architecture, and algorithm. As the energy consumption in complex machine learning tasks keeps increasing exponentially due to larger data sets and resource-constrained edge devices becoming increasingly ubiquitous, neuromorphic computing approaches can be a viable alternative to a deep #convolutional #neural #network that is dominating the field today. In this talk, I introduce neuromorphic computing, outline a few representative examples from different layers of the design stack (devices, circuits, and algorithms) and conclude with a few important challenges and opportunities in this field.”
Learn more and sign up to attend this presentation at events.vtools.ieee.org/m/350928
