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DTSTAMP:20230712T175213Z
LOCATION:Level 2 Lobby
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UID:dac_DAC 2023_sess242_RESEARCH1153@linklings.com
SUMMARY:AutoSparse: Automatic Search for Efficient Activation Sparsity-awa
 re CNN Accelerator
DESCRIPTION:Work-in-Progress Poster\n\nSumin Kim, Taeyun An, and Youngmin 
 Yi (University of Seoul)\n\nRecently, Hardware Architecture Search (HAS) h
 as been actively researched to find optimal architectures for the given mo
 dels with various constraints. However, none of them consider sparsity, to
  the best of our knowledge. In this paper, we propose the activation spars
 ity-aware architecture that can flexibly support various dataflows. We the
 n propose the performance model of the architecture, which is utilized in 
 Evolutionary Algorithm-based design space exploration. With the proposed f
 ramework, the searched dense accelerator for VGG-16 is 1.18x faster than t
 he manually optimized dense accelerator, and the searched sparse accelerat
 or is 1.16x further faster than the searched dense accelerator.\n\nTopic: 
 AI, Autonomous Systems, Cloud, Design, EDA, Embedded Systems, RISC-V, Secu
 rity
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