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An MPSoC for energy-efficient database query processing

Published: 05 June 2016 Publication History

Abstract

This paper presents a heterogeneous database hardware accelerator MPSoC manufactured in 28 nm SLP CMOS. The 18 mm2 chip integrates a runtime task scheduling unit for energy-efficient query processing and hierarchical power management supported by an ultra-fast dynamic voltage and frequency scaling. Four processing elements, connected by a star-mesh network-on-chip, are accelerated by an instruction set extension tailored to fundamental data-intensive applications. We evaluate the MPSoC with typical database benchmarks focusing on scans and bitmap operations. When the processing elements operate on data stored in local memories, the chip consumes 250 mW and shows a 96x energy efficiency improvement compared to state-of-the-art platforms.

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Cited By

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  • (2024)Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy modelFuture Generation Computer Systems10.1016/j.future.2024.04.059159:C(379-394)Online publication date: 1-Oct-2024
  • (2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 7-Dec-2022
  • (2019)Accelerating In-Memory Database Selections Using Latency Masking Hardware ThreadsACM Transactions on Architecture and Code Optimization10.1145/331022916:2(1-28)Online publication date: 9-Apr-2019
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cover image ACM Other conferences
DAC '16: Proceedings of the 53rd Annual Design Automation Conference
June 2016
1048 pages
ISBN:9781450342360
DOI:10.1145/2897937
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 June 2016

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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View all
  • (2024)Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy modelFuture Generation Computer Systems10.1016/j.future.2024.04.059159:C(379-394)Online publication date: 1-Oct-2024
  • (2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 7-Dec-2022
  • (2019)Accelerating In-Memory Database Selections Using Latency Masking Hardware ThreadsACM Transactions on Architecture and Code Optimization10.1145/331022916:2(1-28)Online publication date: 9-Apr-2019
  • (2019)Dynamic Power Management for Neuromorphic Many-Core SystemsIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2019.291189866:8(2973-2986)Online publication date: Aug-2019
  • (2019)Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 PrototypeIEEE Transactions on Biomedical Circuits and Systems10.1109/TBCAS.2019.290640113:3(579-591)Online publication date: Jun-2019
  • (2018)RAPIDProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3190655(1407-1419)Online publication date: 27-May-2018
  • (2017)A Heterogeneous SDR MPSoC in 28 nm CMOS for Low-Latency Wireless ApplicationsProceedings of the 54th Annual Design Automation Conference 201710.1145/3061639.3062188(1-6)Online publication date: 18-Jun-2017
  • (2016)A database accelerator for energy-efficient query processing and optimization2016 IEEE Nordic Circuits and Systems Conference (NORCAS)10.1109/NORCHIP.2016.7792904(1-5)Online publication date: Nov-2016

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