On Predictable Reconfigurable System Design

Nils Voss, Bastiaan Kwaadgras, Oskar Mencer, Wayne Luk, Georgi Gaydadjiev

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We propose a design methodology to facilitate rigorous development of complex applications targeting reconfigurable hardware. Our methodology relies on analytical estimation of system performance and area utilisation for a given specific application and a particular system instance consisting of a controlflow machine working in conjunction with one or more reconfigurable dataflow accelerators. The targeted application is carefully analyzed, and the parts identified for hardware acceleration are reimplemented as a set of representative software models. Next, with the results of the application analysis, a suitable system architecture is devised and its performance is evaluated to determine bottlenecks, allowing predictable design. The architecture is iteratively refined, until the final version satisfying the specification requirements in terms of performance and required hardware area is obtained. We validate the presented methodology using a widely accepted convolutional neural network (VGG-16) and an important HPC application (BQCD). In both cases, our methodology relieved and alleviated all system bottlenecks before the hardware implementation was started. As a result the architectures were implemented first time right, achieving state-of-the-art performance within 15% of our modelling estimations.

Original languageEnglish
Article number17
Number of pages28
JournalACM Transactions on Architecture and Code Optimization
Volume18
Issue number2
DOIs
Publication statusPublished - Mar 2021
Externally publishedYes

Keywords

  • analytical model
  • architecture
  • development methodology
  • FPGA
  • performance model
  • reconfigurable systems

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