A folded 32-bit prefix tree adder in 0.16-μm static CMOS

Author(s):  
A. Goldovsky ◽  
H.R. Srinivas ◽  
R. Kolagotla ◽  
R. Hengst
Keyword(s):  
2018 ◽  
Vol 7 (2.7) ◽  
pp. 647
Author(s):  
J Lakshmi Prasanna ◽  
V Sahiti ◽  
E Raghuveera ◽  
M Ravi Kumar

A 128-Bit Digital Comparator is designed with Digital Complementary Metal Oxide Semiconductor (CMOS) logic, with the use of Parallel Prefix Tree Structure [1] technique. The comparison is performed on Most Significant Bit (MSB) to the Least Significant Bit (LSB). The comparison for the lower order bits carried out only when the MSBs are equal. This technique results in Optimized Power consumption and improved speed of operation. To make the circuit regular, the design is made using only CMOS logic gates. Transmission gates were used in the existing design and are replaced with the simple AND gates. This 128-Bit comparator is designed using Cadence TSMC 0.18µm technology and optimized the Power dissipation to 0.28mW and with a Delay of 0.87μs. 


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 5492-5501 ◽  
Author(s):  
Araf Farayez ◽  
Mamun Bin Ibne Reaz ◽  
Norhana Arsad

Author(s):  
Mengling Feng ◽  
Jinyan Li ◽  
Guozhu Dong ◽  
Limsoon Wong

This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and conciserepresentation- based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed.


2010 ◽  
Vol 44-47 ◽  
pp. 3697-3701
Author(s):  
Wei Liu ◽  
Ling Chen

In order to overcome the shortcomings of traditional algorithms, the algorithm MSPM was proposed. It used longer patterns for mining, which avoided producing lots of patterns with short length. Meanwhile by the use of prefix tree of primary frequent patterns, we extended the primary patterns which avoided plenty of irrelevant patterns. The experimental results show that MSPM not only improves the performance but also achieves effective mining results.


2013 ◽  
Vol 21 (11) ◽  
pp. 1989-1998 ◽  
Author(s):  
Saleh Abdel-Hafeez ◽  
Ann Gordon-Ross ◽  
Behrooz Parhami

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Maan Haj Rachid

The next-generation sequencing (NGS) technology outputs a huge number of sequences (reads) that require further processing. After applying prefiltering techniques in order to eliminate redundancy and to correct erroneous reads, an overlap-based assembler typically finds the longest exact suffix-prefix match between each ordered pair of the input reads. However, another trend has been evolving for the purpose of solving an approximate version of the overlap problem. The main benefit of this direction is the ability to skip time-consuming error-detecting techniques which are applied in the prefiltering stage. In this work, we present and compare two techniques to solve the approximate overlap problem. The first adapts a compact prefix tree to efficiently solve the approximate all-pairs suffix-prefix problem, while the other utilizes a well-known principle, namely, the pigeonhole principle, to identify a potential overlap match in order to ultimately solve the same problem. Our results show that our solution using the pigeonhole principle has better space and time consumption over an FM-based solution, while our solution based on prefix tree has the best space consumption between all three solutions. The number of mismatches (hamming distance) is used to define the approximate matching between strings in our work.


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