Energy Efficient Multistandard Decompressor ASIP

Joost Hoozemans, Kati Tervo, Pekka Jaaskelainen, Zaid Al-Ars

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
16 Downloads (Pure)


Many applications make extensive use of various forms of compression techniques for storing and communicating data. As decompression is highly regular and repetitive, it is a suitable candidate for acceleration. Examples are offloading (de)compression to a dedicated circuit on a heterogeneous System-on-Chip, or attaching FPGAs or ASICs directly to storage so they can perform these tasks on-the-fly and transparently to the application. ASIC or FPGA implementations will usually result in higher energy-efficiency compared to CPUs. Various ASIC and FPGA accelerators have been developed, but they typically target a single algorithm. However, supporting different compression algorithms could be desirable in many situations. For example, the Apache Parquet file format popular in Big Data analytics supports using different compression standards, even between blocks in a single file. This calls for a more flexible software based co-processor approach. To this end, we propose a compiler-supported Application-Specific Instruction-set Processor (ASIP) design that is able to decompress a range of lossless compression standard without FPGA reconfiguration. We perform a case study of searching a compressed database dump of the entire English Wikipedia.

Original languageEnglish
Title of host publicationICCDE 2021 - 2021 7th International Conference on Computing and Data Engineering
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Electronic)978-1-4503-8845-0
Publication statusPublished - 2021
Event7th International Conference on Computing and Data Engineering, ICCDE 2021 - Virtual, Online, Thailand
Duration: 15 Jan 202117 Jan 2021

Publication series

NameACM International Conference Proceeding Series
VolumePart F 174233


Conference7th International Conference on Computing and Data Engineering, ICCDE 2021
CityVirtual, Online


  • ASIP
  • big data
  • FPGA
  • LZ4
  • LZ77
  • Snappy


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