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DESCRIPTION:The exponential growth in the availability of digital data has 
 powered the emergence of data-driven applications like large language mode
 ls\, computer vision\, and digital twin. These applications have incredibl
 y high computing demands that exceed the capabilities of today's high-perf
 ormance computing systems. Unfortunately\, the limitations of scaling sili
 con technology\, and the von-Neumann bottleneck suggest that these demands
  cannot be addressed through traditional means. To address the pressing ch
 allenge of computational scalability and efficiency\, new computing paradi
 gms are being explored. One promising solution to this computational chall
 enge is to perform in-memory computation using emerging non-volatile memor
 y (NVM) devices. This approach enables energy efficient execution of compu
 tationally expensive operations and promises substantial improvements in t
 hroughput. However\, the NVM technology is still in its infancy stage. To 
 fully unleash the promises of in-memory computation systems\, we need nove
 l electronic design automation (EDA) based solutions tailored to data-inte
 nsive applications. In this talk\, I will discuss the role EDA can play to
  close the application-to-devices gap in the in-memory computational stack
 . In the first part of the talk\, I will discuss different in-memory compu
 ting styles in analog and digital domains. In the second part of the talk\
 , I will present my representative projects that are focused on improving 
 the scalability\, energy efficiency\, and robustness of different in-memor
 y computing paradigms. In the final part of the talk\, I will discuss futu
 re research directions for designing next-generation in-memory computing s
 ystems.\n\nSpeaker(s): Muhammad Rashed\, \n\nRoom: EC 550\, Bldg: Engineer
 ing Center\, 115 Library Drive\, Rochester\, Michigan\, United States\, 48
 326\, Virtual: https://events.vtools.ieee.org/m/403709
LOCATION:Room: EC 550\, Bldg: Engineering Center\, 115 Library Drive\, Roch
 ester\, Michigan\, United States\, 48326\, Virtual: https://events.vtools.
 ieee.org/m/403709
ORGANIZER:sharan.kalwani@ieee.org
SEQUENCE:20
SUMMARY:Electronic Design Automation for Next-Generation In-Memory Computin
 g Systems
URL;VALUE=URI:https://events.vtools.ieee.org/m/403709
X-ALT-DESC:Description: <br /><p>The exponential growth in the availability
  of digital data has powered the emergence of data-driven applications lik
 e large language models\, computer vision\, and digital twin. These applic
 ations have incredibly high computing demands that exceed the capabilities
  of today's high-performance computing systems. Unfortunately\, the limita
 tions of scaling silicon technology\, and the von-Neumann bottleneck sugge
 st that these demands cannot be addressed through traditional means. To ad
 dress the pressing challenge of computational scalability and efficiency\,
  new computing paradigms are being explored. One promising solution to thi
 s computational challenge is to perform in-memory computation using emergi
 ng non-volatile memory (NVM) devices. This approach enables energy efficie
 nt execution of computationally expensive operations and promises substant
 ial improvements in throughput. However\, the NVM technology is still in i
 ts infancy stage. To fully unleash the promises of in-memory computation s
 ystems\, we need novel electronic design automation (EDA) based solutions 
 tailored to data-intensive applications. In this talk\, I will discuss the
  role EDA can play to close the application-to-devices gap in the in-memor
 y computational stack. In the first part of the talk\, I will discuss diff
 erent in-memory computing styles in analog and digital domains. In the sec
 ond part of the talk\, I will present my representative projects that are 
 focused on improving the scalability\, energy efficiency\, and robustness 
 of different in-memory computing paradigms. In the final part of the talk\
 , I will discuss future research directions for designing next-generation 
 in-memory computing systems.</p>
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