Data Compression and Network Processing for Polymorphous Computing Architecture (PCA)

Abstract

The goal of this project was to explore the appropriateness of the University of Texas at Austin's TRIPS architecture for Embedded/Networking applications in a polymorphic computing setting. This involved developing an Embedded/Networking Morph mode for TRIPS, or EN-Morph. Early studies found that key architectural features of TRIPS would need to be rethought since TRIPS is geared towards high productivity computing. Goals of embedded computing contrasted with high productivity computing in areas such as cost, power consumption, etc. In reaction, the EN-Morph team developed an embedded TRIPS architecture that was appropriate for embedded/networking, and then fed these ideas to the TRIPS team. In the end, CLAW, a scalable, synthesizeable TRIPS core processor with low power characteristics, and two hardware accelerators to off-load the core from compute-intensive tasks were created. A set of networking benchmarks for the entire DARPA Polymorphous Computing Architectures (PCA) program was also created that encourage networking aspects to be considered in PCA designs.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2005
Accession Number
ADA434236

Entities

People

  • Balaji V. Iyer
  • J. M. Edwards
  • Meeta Yadav
  • Monther Aldwairi
  • Paul D Franzon
  • Shobhit Kanaujia
  • Thomas M. Conte

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Compression
  • Computer Architecture
  • Computers
  • Computing System Architectures
  • Data Compression
  • Energy Consumption
  • Engineering
  • Information Processing
  • Information Systems
  • Instruction Set Architecture
  • Intrusion Detection
  • Network Protocols
  • Technology Transfer
  • Trees (Data Structures)
  • Universities

Fields of Study

  • Computer science

Readers

  • Military Logistics and Supply Chain Management
  • Parallel and Distributed Computing.