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UID:5847-1638270000-1638273600@seasevents.nmsdev7.com
SUMMARY:ESE Fall Colloquium Seminar -  "Bits and Brains: Ultra-low Power\, Neuro-inspired Edge-AI for Autonomous Systems"
DESCRIPTION:As we march towards the age of “ubiquitous intelligence”\, we note that AI and Machine learning are progressively moving from the Cloud to the Edge devices. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning\, in hardware-constrained ultra-low-power (uW to mW) systems – an area of active research. In this talk\, I will discuss the promises and outlook of Edge-AI and their applications in Autonomous Systems; and elaborate on some of our recent work on enabling such systems in sensor nodes and robotics. While some of these systems extend our understanding of statistical machine learning\, a large class of circuits and systems are inspired by the information representation in the brain. I will talk about the design of such circuits and systems with an emphasis on the impact of mixed-signal circuits\, near-memory and in-memory compute architectures\, non-CMOS (RRAM-based) compute macros\, as well as algorithm-hardware co-design to realize the most energy-efficient Edge-AI ASICs for the next generation of smart and autonomous systems.
URL:https://seasevents.nmsdev7.com/event/ese-fall-colloquium-seminar-bits-and-brains-ultra-low-power-neuro-inspired-edge-ai-for-autonomous-systems/
LOCATION:Zoom – Meeting ID 912 5944 4192
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
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