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DTSTAMP:20260422T143141Z
LOCATION:B309
DTSTART;TZID=America/New_York:20241118T141200
DTEND;TZID=America/New_York:20241118T141500
UID:submissions.supercomputing.org_SC24_sess753_ws_whpc103@linklings.com
SUMMARY:High Performance Computing Enabled Through Neuromorphic Systems an
 d In-Memory Computing Primitives
DESCRIPTION:Aishwarya Natarajan (Hewlett Packard Labs)\n\nWith the rise of
  data driven workloads and increase in model sizes, it has become increasi
 ngly critical to build computationally efficient hardware. Drawing inspira
 tion from the brain to harness the computational advantages of such a neur
 al structure, fundamental blocks of neurons and synapses are built to impl
 ement neuromorphic systems. We discuss techniques to demonstrate energy-ef
 ficient computing on analog configurable platforms to enable real-time sys
 tems on hardware. Further, we show tree-based machine learning models thro
 ugh In-Memory Computing (IMC) primitives such as Analog Content Addressabl
 e Memories (ACAMs) that are designed with emerging non-volatile technologi
 es such as memristors. Such systems ultimately pave the path to take physi
 cal approaches to build large-scale systems for high performance computing
  in a holistic manner.\n\nTag: Broader Engagement, HPC in Society, Inclusi
 vity\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Elsa J
 . Gonsiorowski (Lawrence Livermore National Laboratory (LLNL)) and Mozhgan
  Kabiri chimeh (NVIDIA Corporation)\n\n
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