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DTSTART;TZID=America/Denver:20231116T133000
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UID:submissions.supercomputing.org_SC23_sess184@linklings.com
SUMMARY:Fault Tolerance and FPGA Codesign
DESCRIPTION:Demystifying and Mitigating Cross-Layer Deficiencies of Soft E
 rror Protection in Instruction Duplication\n\nSoft errors are prevalent in
  modern High-Performance Computing (HPC) systems, resulting in silent data
  corruptions (SDCs), compromising system reliability. Instruction duplicat
 ion is a widely used software-based protection technique against SDCs. Exi
 sting instruction duplication techniques are mostl...\n\n\nZhengyang He an
 d Yafan Huang (University of Iowa); Hui Xu (Fudan University, Shanghai); D
 ingwen Tao (Indiana University); and Guanpeng Li (University of Iowa)\n---
 ------------------\nCo-Design Hardware and Algorithm for Vector Search\n\n
 Vector search has emerged as the foundation for large-scale information re
 trieval and machine learning systems, with search engines like Google and 
 Bing processing tens of thousands of queries per second on petabyte-scale 
 document datasets by evaluating vector similarities between encoded query 
 text...\n\n\nWenqi Jiang (ETH Zurich - Swiss Federal Institute of Technolo
 gy); Shigang Li (Beijing University of Posts and Telecommunications); Yu Z
 hu, Johannes de Fine Licht, Zhenhao He, and Runbin Shi (ETH Zurich - Swiss
  Federal Institute of Technology); Cedric Renggli (Apple Inc); Shuai Zhang
  (ETH Zurich - Swiss Federal Institute of Technology); Theodoros Rekatsina
 s (Apple Inc); and Torsten Hoefler and Gustavo Alonso (ETH Zurich - Swiss 
 Federal Institute of Technology)\n---------------------\nStructural Coding
 : A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults\n
 \nThe advent of High Performance Computing has led to the adoption of Conv
 olutional Neural Networks (CNNs) in safety-critical applications such as a
 utonomous vehicles. However, CNNs are vulnerable to DRAM errors corrupting
  their parameters, thereby degrading their accuracy. Existing techniques f
 or pro...\n\n\nAli Asgari Khoshouyeh (University of British Columbia); Flo
 rian Geissler, Seyed Qutub, and Michael Paulitsch (Intel Corporation); and
  Prashant Nair and Karthik Pattabiraman (University of British Columbia)\n
 \nTag: Accelerators, Artificial Intelligence/Machine Learning, Codesign, F
 ault Handling and Tolerance, Performance Measurement, Modeling, and Tools,
  Post-Moore Computing\n\nRegistration Category: Tech Program Reg Pass\n\nR
 eproducibility Badges: Artifact Available, Artifact Functional\n\nSession 
 Chair: Lishan Yang (George Mason University (GMU))
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