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DTSTAMP:20260404T083433
CREATED:20230517T170634Z
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UID:9141-1685455200-1685462400@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Compute-In-Memory on Emerging Memory Technology: From Device to Algorithm"
DESCRIPTION:Current computing systems are mainly constructed on the von Neumann architecture\, where data needs to be transferred to a processing unit from memory components. The latency associated with accessing data from the memory units is a key performance bottleneck for a range of data-intensive applications in the convergence of big data and AI. Several solutions have been proposed to mitigate and overcome this bottleneck\, with a prominent one being placing memory and logic units in close physical proximity. While significant progress has been made along those lines at both technology and architecture levels\, a transformative approach would be to implement arithmetic kernels precisely where the data are stored using memory devices. This is known as compute-in-memory (CIM). \nIn this dissertation\, I will begin by presenting the most recent advancements in the CMOS-compatible ferroelectric memory technologies on aluminum nitride platform. Second\, I will present a reconfigurable CIM system on field-programmable ferroelectric diodes in a transistor-free architecture\, allowing for multiple essential data operations. Last\, I will discuss the conceptualization and demonstration of a programmable parallel search architecture – analog content-addressable memory (ACAM) on complementary Si-CMOS ferroelectric field-effect-transistor memory. The deployment and acceleration of deep neural network and kernel regression on ACAM will also be presented.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-compute-in-memory-on-emerging-memory-technology-from-device-to-algorithm/
LOCATION:Room 313\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
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