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DTSTART:20260308T030000
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DTSTART;TZID=America/Los_Angeles:20260624T185000
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DESCRIPTION:In this talk\, I will talk about some hardware/software work my
  group has done in the area of stochastic computing-based machine learning
  acceleration. Stochastic computing or SC is an approximate\, stream-based
  computing paradigm enabling extremely area-efficient implementations of b
 asic arithmetic operations such as multiplication and addition. I will tal
 k about the suitability of the SC to the machine learning/event processing
  workloads\, how to deal with its inherent approximate nature and briefly 
 discuss few chip prototypes that leverage both logic and in-memory impleme
 ntations of SC-based accelerators for dense as well as a sparse compute.\n
 \nSpeaker(s): Puneet Gupta\, \n\nVirtual: https://events.vtools.ieee.org/m
 /561732
LOCATION:Virtual: https://events.vtools.ieee.org/m/561732
ORGANIZER:swapnilsayansaha@ieee.org
SEQUENCE:9
SUMMARY: Efficient Stochastic Machine Learning at the Edge
URL;VALUE=URI:https://events.vtools.ieee.org/m/561732
X-ALT-DESC:Description: <br /><p>In this talk\, I will talk about some hard
 ware/software work my group has done in the area of stochastic computing-b
 ased machine learning acceleration. Stochastic computing or SC is an appro
 ximate\, stream-based computing paradigm enabling extremely area-efficient
  implementations of basic arithmetic operations such as multiplication and
  addition. I will talk about the suitability of the SC to the machine lear
 ning/event processing workloads\, how to deal with its inherent approximat
 e nature and briefly discuss few chip prototypes that leverage both logic 
 and in-memory implementations of SC-based accelerators for dense as well a
 s a sparse compute.&nbsp\;</p>
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