Graviton: A Reconfigurable Memory-Compute Fabric for Data Intensive Applications

Ashutosh Dhar, Paul Reckamp, Jinjun Xiong, Wen mei Hwu, Deming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The rigid organization and distribution of computational and memory resources often limits how well accelerators can cope with changing algorithms and increasing dataset sizes and limits how efficiently they use their computational and memory resources. In this work, we leverage a novel computing paradigm and propose a new memory-based reconfigurable fabric, Graviton. We demonstrate the ability to dynamically trade memory for compute and vice versa, and can tune the architecture of the underlying hardware to suit the memory and compute requirements of the application. On a die-to-die basis, Graviton provides up to 47X more on-chip memory capacity over an Alveo U250 SLR, with just an additional 1.7 % area on a die-to-die basis than modern FPGAs, and is 28.7X faster, on average, on a range of compute and data intensive tasks.

Original languageEnglish (US)
Title of host publicationApplied Reconfigurable Computing. Architectures, Tools, and Applications - 17th International Symposium, ARC 2021, Proceedings
EditorsSteven Derrien, Frank Hannig, Pedro C. Diniz, Daniel Chillet
Number of pages11
ISBN (Print)9783030790240
StatePublished - 2021
Event17th International Symposium on Applied Reconfigurable Computing, ARC 2021 - Virtual, Online
Duration: Jun 29 2021Jun 30 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12700 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Symposium on Applied Reconfigurable Computing, ARC 2021
CityVirtual, Online


  • Logic folding
  • Reconfigurable architectures

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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