A Reconfigurable Bloom Filter Architecture for BLASTN

2009 ◽  
pp. 40-49 ◽  
Author(s):  
Yupeng Chen ◽  
Bertil Schmidt ◽  
Douglas L. Maskell
Keyword(s):  
Bloom Filter ◽  
Filter Architecture

scholarly journals A New Bloom Filter Architecture for FIB Lookup in Named Data Networking

2019 ◽  
Vol 9 (2) ◽  
pp. 329 ◽  
Author(s):  
Hayoung Byun ◽  
Hyesook Lim
Keyword(s):  
Network Traffic ◽  
High Speed ◽  
Bloom Filter ◽  
Future Internet ◽  
Bloom Filters ◽  
Named Data Networking ◽  
Future Internet Architecture ◽  
Massive Growth ◽  
Filter Architecture ◽  
Data Networking

Network traffic has increased rapidly in recent years, mainly associated with the massive growth of various applications on mobile devices. Named data networking (NDN) technology has been proposed as a future Internet architecture for effectively handling this ever-increasing network traffic. In order to realize the NDN, high-speed lookup algorithms for a forwarding information base (FIB) are crucial. This paper proposes a level-priority trie (LPT) and a 2-phase Bloom filter architecture implementing the LPT. The proposed Bloom filters are sufficiently small to be implemented with on-chip memories (less than 3 MB) for FIB tables with up to 100,000 name prefixes. Hence, the proposed structure enables high-speed FIB lookup. The performance evaluation result shows that FIB lookups for more than 99.99% of inputs are achieved without needing to access the database stored in an off-chip memory.

scholarly journals Fully pipelined bloom filter architecture

2008 ◽  
Vol 12 (11) ◽  
pp. 855-857 ◽  
Author(s):  
Michael Paynter ◽  
Taskin Kocak
Keyword(s):  
Bloom Filter ◽  
Filter Architecture

scholarly journals COUNTING BLOOM FILTER ARCHITECTURE IN VLSI NETWORK SYSTEMS

2015 ◽  
pp. 76-80
Author(s):  
NAGAMALLI. A ◽  
KEDARESWARARAO. M
Keyword(s):  
Bloom Filter ◽  
Embedded Memory ◽  
Network Systems ◽  
False Negatives ◽  
Space Efficiency ◽  
Vlsi Technology ◽  
Set Membership ◽  
Filter Architecture ◽  
Real Time Applications ◽  
Counting Bloom Filter

The Counting Bloom Filter (CBF) is useful for real time applications where the time and space efficiency is the main consideration in performing a set membership tests. The CBF estimates whether an element is present in a large array or not by allowing false positives and by not permitting false negatives. In this paper CBF architecture is analyzed and has been implemented. There are two approaches of CBF, SRAM based approach using up/down counters and the LCBF using up/down LFSR unit. In this paper the LCBF architecture discussed and analyzed. In the latest VLSI technology it is easy to fabricate memories that hold a few million bits of data and addresses. But in the recent embedded memory technologies rather than mapping of addresses of 5000 bits of data using hashing functions we can concise in to single contiguous memory.

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

Enhancing the Performance of One Dimensional Median Filter

2019 ◽  
Vol 118 (7) ◽  
pp. 73-76
Author(s):  
Sharanabasappa ◽  
P Ravibabu
Keyword(s):  
Impulse Noise ◽  
Rank Order ◽  
Median Filter ◽  
Filter Method ◽  
Salt And Pepper Noise ◽  
Edge Preservation ◽  
Word Level ◽  
Look Ahead ◽  
Filter Architecture ◽  
Salt And Pepper

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter  in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

P2P Probabilistic Routing Algorithm Based on Data Copying and Bloom Filter

2011 ◽  
Vol 22 (4) ◽  
pp. 773-781
Author(s):  
Gui-Ming ZHU ◽  
De-Ke GUO ◽  
Shi-Yao JIN
Keyword(s):  
Routing Algorithm ◽  
Bloom Filter ◽  
Probabilistic Routing
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