Title
Instruction level redundant number computations for fast data intensive processing in asynchronous processors
Abstract
Instruction level parallelism (ILP) is strictly limited by various dependencies. In particular, data dependency is a major performance bottleneck of data intensive applications. In this paper we address acceleration of the execution of instruction codes serialized by data dependencies. We propose a new computer architecture supporting a redundant number computation at the instruction level. To design and implement the scheme, an extended data-path and additional instructions are also proposed. The architectural exploitation of instruction level redundant number computations (IL-RNC) makes it possible to eliminate carry propagations. As a result execution of instructions which are serialized due to inherent data dependencies is accelerated. Simulations have been performed with data intensive processing benchmarks and the proposed architecture shows about a 1.2-1.35 fold speedup over a conventional counterpart. The proposed architecture model can be used effectively for data intensive processing in a microprocessor, a digital signal processor and a multimedia processor.
Year
DOI
Venue
2005
10.1016/j.sysarc.2004.09.005
Journal of Systems Architecture
Keywords
Field
DocType
data intensive processing benchmarks,microprocessor architecture,instruction level,inherent data dependency,asynchronous processor,data intensive application,data intensive processing,data dependency,redundant number computation,instruction level redundant number,additional instruction,instruction code,asynchronous circuits,instruction level parallelism,fast data,asynchronous circuit,digital signal processor,computer architecture
Instruction-level parallelism,Asynchronous communication,Bottleneck,Data dependency,Computer science,Digital signal processor,Parallel computing,Microprocessor,Transport triggered architecture,Real-time computing,Speedup
Journal
Volume
Issue
ISSN
51
3
Journal of Systems Architecture
Citations 
PageRank 
References 
0
0.34
12
Authors
3
Name
Order
Citations
PageRank
Jeong-Gun Lee17218.27
Euiseok Kim24610.09
Dongik Lee37714.46