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Cloud Building Block Chip for Creating FPGA and ASIC Clouds

Published:01 December 2021Publication History
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Abstract

Hardware-accelerated cloud computing systems based on FPGA chips (FPGA cloud) or ASIC chips (ASIC cloud) have emerged as a new technology trend for power-efficient acceleration of various software applications. However, the operating systems and hypervisors currently used in cloud computing will lead to power, performance, and scalability problems in an exascale cloud computing environment. Consequently, the present study proposes a parallel hardware hypervisor system that is implemented entirely in special-purpose hardware, and that virtualizes application-specific multi-chip supercomputers, to enable virtual supercomputers to share available FPGA and ASIC resources in a cloud system. In addition to the virtualization of multi-chip supercomputers, the system’s other unique features include simultaneous migration of multiple communicating hardware tasks, and on-demand increase or decrease of hardware resources allocated to a virtual supercomputer. Partitioning the flat hardware design of the proposed hypervisor system into multiple partitions and applying the chip unioning technique to its partitions, the present study introduces a cloud building block chip that can be used to create FPGA or ASIC clouds as well. Single-chip and multi-chip verification studies have been done to verify the functional correctness of the hypervisor system, which consumes only a fraction of (10%) hardware resources.

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            • Published in

              cover image ACM Transactions on Reconfigurable Technology and Systems
              ACM Transactions on Reconfigurable Technology and Systems  Volume 15, Issue 2
              June 2022
              310 pages
              ISSN:1936-7406
              EISSN:1936-7414
              DOI:10.1145/3501287
              • Editor:
              • Deming Chen
              Issue’s Table of Contents

              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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              Publication History

              • Published: 1 December 2021
              • Accepted: 1 May 2021
              • Revised: 1 April 2021
              • Received: 1 January 2021
              Published in trets Volume 15, Issue 2

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