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• W4312998584 abstract "Deep Neural Network (DNN) INFerence-as-a-Service (INFaaS) is the dominating workload in current data centers, for which FPGAs become promising hardware platforms because of their high flexibility and energy efficiency. The dynamic and multi-tenancy nature of INFaaS requires careful design in three aspects: multi-tenant architecture, multi-DNN scheduling, and multi-core mapping. These three factors are critical to the system latency and energy efficiency but are also challenging to optimize since they are tightly coupled and correlated. This paper proposes <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>H3M</b> , an automatic Design Space Exploration (DSE) framework to jointly optimize the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>architecture</i> , <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>scheduling</i> , and <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>mapping</i> for serving INFaaS on cloud FPGAs. H3M explores: (1) the architecture design space with <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>H</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>eterogeneous</i> spatial <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>M</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ulti-tenant</i> sub-accelerators, (2) layer-wise scheduling for <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>H</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>eterogeneous</i> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>M</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ulti-DNN</i> workloads, and (3) single-layer mapping to the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>H</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>omogeneous</i> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>M</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ulti-core</i> architecture. H3M beats state-of-the-art multi-tenant DNN accelerators, Planaria and Herald, by up to 7.5× and 3.6× in Energy-Delay-Product (EDP) reduction on the ASIC platform. On the Xilinx U200 and U280 FPGA platforms, H3M offers 2.1-5.7× and 1.8-9.0× EDP reduction over Herald." @default.
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• W4312998584 date "2023-05-01" @default.
• W4312998584 modified "2023-10-14" @default.
• W4312998584 title "Serving Multi-DNN Workloads on FPGAs: A Coordinated Architecture, Scheduling, and Mapping Perspective" @default.
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• W4312998584 doi "https://doi.org/10.1109/tc.2022.3214113" @default.
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