SIMD processor arrays for image and video processing: A review

Thinh M. Le, W. M. Snelgrove, S. Panchanathan

Research output: Contribution to journalConference article

7 Scopus citations

Abstract

SIMD processor arrays are becoming popular for their fast parallel executions of low- to medium-complexity image and video processing algorithms, and most stages of the compression standards (JPEG, H.263, and MPEG's). In many existing techniques, visual data processing algorithms and compression standards possess a high degree of parallelism. In particular, the processing of a certain pixel/block does not generally require data from a distant pixel/block, and the instructions for processing these pixels/blocks are usually identical. Thus, these algorithms map naturally onto the architecture of the SIMD processor arrays. In this paper, the architectures of the recently SIMD processor arrays will be reviewed together with algorithms demonstrating their superior features. Due to the length of the paper, only processor arrays implemented at the chip-level are discussed, especially those whose logic circuits are merged/embedded in the SRAM or DRAM memory process. While some processor arrays are designed by embedding the memory modules onto the existing processors, a significant number of processor arrays are realized by integrating logic circuits onto existing RAM (logic-in-memory) to save the inherently large memory bandwidth, and thus achieving a performance in the order of Tera instructions per second.

Original languageEnglish (US)
Pages (from-to)30-41
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3311
DOIs
StatePublished - Dec 1 1998
Externally publishedYes
EventMultimedia Hardware Architectures 1998 - San Jose, CA, United States
Duration: Jan 29 1998Jan 30 1998

Keywords

  • Logic-in-memory
  • Parallel processing
  • Processor arrays
  • SIMD architecture

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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