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SUMMARY:Near-Memory Data Services
DTSTART:20170622T140000
DTEND:20170622T160000
DTSTAMP:20260428T111519Z
UID:1947079443d29d1ce0483bcca8e4882b9f4da3733e0cfe3b51be876b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Hussein Kassir\nEDIC candidacy exam\nExam president: Prof. Jam
 es Larus\nThesis advisor: Prof. Babak Falsafi\nCo-examiner: Prof. Paolo Ie
 nne\n\nAbstract\nModern data analytics operate on huge in-memory datasets.
  The high-energy cost of moving data from memory to CPU has become a major
  concern with the end of Dennard scaling. Near-memory processing (NMP) has
  become an interesting direction for data analytics to reduce the cost of 
 data movement\, especially after breakthrough in 3D-Stack technology that 
 allows tight integration of logic and memory. We present three different d
 esign alternatives for NMP data analytics. The designs focus on the tradeo
 ffs between programmability and performance under tight NMP constraints. W
 e discuss each design's architecture\, advantages\, and limitations. We fi
 nally propose a plan that builds upon the previous work to produce a high 
 performance NMP system that targets various data analytics workloads.\n\nB
 ackground papers\nPractical Near Data Processing \,Mingyu Gao\, Grant Ayer
 s\, Christos Kozyrakis\, Stanford edu.\nHRL: Efficient and Flexible Reconf
 igurable Logic for Near-Data Processing\, Mingyu Gao and Christos Kozyraki
 s\, Stanford edu\nThe Mondrian Data Engine\, Drumond E.\, et al.\n 
LOCATION:BC 329 https://plan.epfl.ch/?room==BC%20329
STATUS:CONFIRMED
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