Thesis
High-performance hybrid wave-pipeline scheme as it applies to adder micro-architectures
Master of Science (MS), Washington State University
2005
Handle:
https://hdl.handle.net/2376/349
Abstract
Pipelining digital systems has been shown to provide significant performance gains over nonpipelined systems and remains a standard in microprocessor/digital design. The desire for increased performance has led to research on deeper pipelines and new pipelining architectures such as wave-pipelining and hybrid wave-pipelining. In this thesis a hybrid wave-pipelined parallel adder is presented and compared to conventional- and wave-pipelined parallel adders. The comparison shows that the hybrid wave-pipelined adder operates at frequencies 19% and 167% faster than wave-pipelining and conventional pipelining (when the same stage partitioning is used) respectively. A performance estimation shows that if a deep conventional pipelined adder is implemented the hybrid wave-pipelined adder still outperforms a super-pipelined adder by 42%. Performance is the main benefit of using hybrid wave-pipelining. Other benefits may include lessening the clock skew and clock distribution delays, the ability to sustain a greater number of data waves within the pipe and the ability to easily perform clock gating. This thesis also presents a novel hybrid ripple carry-/carry lookahead-adder (RCA/CLA) adder that uses a prediction scheme to calculate the carry. Simulation results have shown the prediction scheme outperforms a traditional RCA/CLA by 22%-67% with only a 1.5% increase in power. The scheme reduces the transistor count by 15% per CLA block.
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Details
- Title
- High-performance hybrid wave-pipeline scheme as it applies to adder micro-architectures
- Creators
- James E. Levy
- Contributors
- Jabulani Nyathi (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Identifiers
- 99900525288301842
- Language
- English
- Resource Type
- Thesis