Processor design based on dataflow concurrency

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4 Scopus citations


This paper presents new architectural concepts for uniprocessor system designs. They result in a uniprocessor design that conforms to the data-driven (i.e. dataflow) computation paradigm. It is shown that usage of this, namely D2-CPU (Data-Driven) processor, follows the natural flow of programs, minimizes redundant (micro)operations, lowers the hardware cost, and reduces the power consumption. We assume that programs are developed naturally using a graphical or equivalent language that can explicitly show all data dependencies. Instead of giving the CPU the privileged right of deciding what instructions to fetch in each cycle (as is the case for CPUs with a program counter), instructions are entering the CPU when they are ready to execute or when all their operand(s) are to be available within a few clock cycles. This way, the application-knowledgeable algorithm, rather than the application-ignorant CPU, is in control. The CPU is used just as a resource, the way it should normally be. This approach results in outstanding performance and elimination of large numbers of redundant operations that plague current processor designs. The latter, conventional CPUs are characterized by numerous redundant operations, such as the first memory cycle in instruction fetching that is part of any instruction cycle, and instruction and data prefetchings for instructions that are not always needed. A comparative analysis of our design with conventional designs proves that it is capable of better performance and simpler programming. Finally, VHDL implementation is used to prove the viability of this approach.

Original languageEnglish (US)
Pages (from-to)199-220
Number of pages22
JournalMicroprocessors and Microsystems
Issue number4
StatePublished - May 20 2003

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


  • Comparative analysis
  • Concurrent operations
  • Data-driven (dataflow) model of computation
  • Distributed CPU
  • Processor design


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