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Designing with extreme parallelism

Published: 24 February 2008 Publication History

Abstract

Modern FPGAs can implement large, custom compute engines that are designed to exploit extreme amounts of parallel computation. Through parallelism, these systems achieve orders of magnitude higher performance than the fastest microprocessors. Building such custom compute engines with existing hardware design languages is too difficult and time-consuming. For this to become mainstream technology, the task of designing such parallel systems must be as simple as possible. Thus, high-level languages are needed which can specify a custom compute engine or be compiled to run on predesigned parallel systems. In this workshop, we will examine several approaches for specifying extremely parallel computations in high-level languages. These can be used to build parallel systems in FPGAs, or they can be used to specify parallel computations in other competing architectures. By examining several different approaches, one gains insight into the best approach for solving a given problem. Ideally, this will also inspire new approaches for designing with extreme parallelism

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  • (2017)FPGA-Based Implementation of Kalman Filter for Real-Time Estimation of Tire Velocity and AccelerationIEEE Sensors Journal10.1109/JSEN.2017.272652917:17(5749-5758)Online publication date: 1-Sep-2017

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cover image ACM Conferences
FPGA '08: Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
February 2008
278 pages
ISBN:9781595939340
DOI:10.1145/1344671
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2008

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Author Tags

  1. FPGA
  2. custom compute engine
  3. hardware description language
  4. high-level electronic design
  5. parallel processing
  6. reconfigurable computing

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FPGA08
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Cited By

View all
  • (2017)FPGA-Based Implementation of Kalman Filter for Real-Time Estimation of Tire Velocity and AccelerationIEEE Sensors Journal10.1109/JSEN.2017.272652917:17(5749-5758)Online publication date: 1-Sep-2017

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