Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Ultra-Fast Data-Mining Hardware Architecture Based on Stochastic Computing

  • Antoni Morro ,

    Contributed equally to this work with: Antoni Morro, Vincent Canals, Josep L. Rossello

    Affiliation Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain

  • Vincent Canals ,

    Contributed equally to this work with: Antoni Morro, Vincent Canals, Josep L. Rossello

    Affiliation Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain

  • Antoni Oliver ,

    ‡ These authors also contributed equally to this work.

    Affiliation Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain

  • Miquel L. Alomar ,

    ‡ These authors also contributed equally to this work.

    Affiliation Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain

  • Josep L. Rossello

    Contributed equally to this work with: Antoni Morro, Vincent Canals, Josep L. Rossello

    Affiliation Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain

Ultra-Fast Data-Mining Hardware Architecture Based on Stochastic Computing

  • Antoni Morro, 
  • Vincent Canals, 
  • Antoni Oliver, 
  • Miquel L. Alomar, 
  • Josep L. Rossello


Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition analysis of huge database sets. The proposed technique consists in the hardware implementation of a parallel architecture implementing a similarity search of data with respect to different pre-stored categories. We design pulse-based stochastic-logic blocks to obtain an efficient pattern recognition system. The proposed architecture speeds up the screening process of huge databases by a factor of 7 when compared to a conventional digital implementation using the same hardware area.


Data explosion is the capability of current technologies to generate large amounts of data at different scientific disciplines. Data volumes are doubling every year in most areas of modern science [1], and its proper analysis is becoming more and more complex. As a matter of fact, data explosion has not led to an information explosion since current data analysis techniques are unable to handle billions of data records in a reasonable period of time. Large scientific databases containing several terabytes of information need to be continuously screened by scientists, and current processor-based techniques are unable to provide an efficient response to this problem. To solve this, different solutions have been developed based on artificial neural networks [25], the use of simple metrics [6] or the extraction of simplified datasets from the original data [7].

An alternative to traditional deterministic computational methodologies is the use of stochastic logic, introduced more than 40 years ago [8,9]. Stochastic computing is the result of applying probabilistic laws to logic cells where variables are represented by random pulse streams, thus providing a natural way of representing analog quantities with digital systems [10]. Pulses can be converted to binary numbers by using digital counters (P2B converters) while binary numbers can be translated to stochastic signals by combining a random (or a pseudo-random) number generator and a comparator (B2P converters). Stochastic computing makes use of digital technology to perform complex arithmetic operations with a reduced number of gates. When performing those types of operations, stochastic signals must be completely uncorrelated for a proper behavior.

Stochastic computing is useful for those applications requiring parallel-processing techniques [1114]. Traditional parallel processing architectures have the shortcoming of requiring a large amount of hardware resources. Therefore, the number of tasks that can be executed in parallel within a single chip is relatively small. Stochastic computing could represent a solution to this problem since the hardware used to solve each task is reduced in size if compared to traditional digital implementations. As a result, more complex tasks can be executed in parallel when using stochastic computing elements. The greatest advantage of stochastic computing is the potential of implementing hundreds of smart computing elements in one single integrated circuit, thus obtaining a highly parallelized processing chip with a computing capacity that can be several orders of magnitude higher than traditional binary-logic-based microprocessors.

In this work we generalize the basic stochastic computing principles, thus creating a more general probabilistic processing. The primary basic principle of traditional stochastic computing is that the stochastic bit streams must be completely uncorrelated in time and space to obtain the desired arithmetic operations when using simple logic gates. Our extended probabilistic processing approach will also use correlated signals in order to implement a set of non-linear operations. This set of non-linear operations can be used for an efficient and fast comparison between signals as we will show later.

Accordingly, probabilistic computing is characterized by the combination of two different types of operations:

  • Arithmetic operations implemented by uncorrelated signals (such as the multiplication, the division or the addition). These ones are taken from the traditional stochastic computing concepts [8].
  • Non-linear operations performed by correlated signals evaluated through logic gates.

The introduction of the possibility of coherence between the signal phases enhances the capacity of stochastic computing by including those non-linear operations that can be applied to perform fast similarity searches. Consequently, complex pattern recognition tasks can be executed in parallel when using probabilistic processing operations.

In this work we present a highly efficient methodology for data mining based on probabilistic processing. High dimensional data is inherently complex in clustering, classification and similarity search [15]. The proposed approach is evaluated showing its application to a similarity search over a huge database. Most data mining algorithms use similarity search as a subroutine core [1618], and thus the time taken for this task is the bottleneck of virtually all data mining algorithms [19]. Similarity search plays a fundamental role in many data mining and machine learning problems, e.g. text categorization [20], collaborative filtering [21], time-series analysis [22,23], protein sequencing [24] or any application-specific task as petroglyphs comparison [25]. At the same time, the mining of huge datasets implies the use of large computer clusters [26,27]. The proposed approach based on the use of probabilistic processing shows large improvements in terms of hardware resources when compared with conventional solutions.

Basic Principles of Probabilistic Processing

Stochastic computing principles

In stochastic-based computations a global clock provides the time interval during which all stochastic signals are stable (settled to 0 or 1). For each clock cycle, a particular node has a probability p of being in the HIGH state (see Fig 1). Stochastic pulsed signals follow probabilistic laws when evaluated with logic gates. As an example, an AND gate provides at the output the product of its inputs (that is to say, the collision probability between signals) whereas a NOT gate converts a probability p at the input to the complementary 1-p at the output. One of the requirements for these stochastic computing blocks is that signals must be un-correlated at different clock cycles and between them. In Fig 2 we show the importance of the temporal de-correlation when implementing arithmetic functions (we use the example of implementing f(p) = p(1-p)). The figure illustrates that if the inputs of the AND gate p and 1-p are correlated the output is always equal to zero. Such correlations can be eliminated using shift registers to delay signals from one arithmetic level to the next one. In the correct case, the AND gate evaluates properly the product between p and a delayed (and therefore uncorrelated) value of 1-p.

Fig 1. Basic temporal behavior of stochastic signals.

There is no correlation between the signal values at different clock cycles. The number associated with the stochastic signal is the activation probability.

Fig 2. Correlation between signals and mathematical relationship between inputs and outputs.

If we desire to implement the function p(1-p) we must add a delaying element to de-correlate signals at the input of the AND gate.

Random number generation

The generation of pseudorandom sequences is a key issue for the implementation of probabilistic computing systems since stochastic bit streams are required to convert binary magnitudes to their equivalent stochastic signals. In particular, in order to obtain a random variable from Bernoulli sequences with a known generating probability p, we have to compare the binary number to convert (P) and the generated random number (R). If P>R, the stochastic pulse will be at high level, otherwise it will be low (Fig 3a).

Fig 3.

(a) Binary to pulse converter (B2P), an LFSR and a comparator are combined to obtain the pulsed signal. (b) Linear Feedback Shift Register (LFSR) used in the experiments.

A commonly used [28] source of pseudorandom digital noise is the linear feedback shift register (LFSR), which is an array of interconnected flip-flops with feedback to its input from a combination of the outputs of its various stages gated together in EXCLUSIVE-OR gates (see Fig 3b). This linear feedback structure provides uniformly distributed sequences (which have an autocorrelation delta function), but with a finite period of repetition, which has an exponential dependence with the number of bits. Since the sequences are produced deterministically, uncorrelated sources must be generated using different initial values (seeds) for the registers. The feedback configurations enabling maximal-length generators have been given [29,30]. The configuration applied in the present work, which employs a 8-bit shift register, is shown in Fig 3b. The autocorrelation function of a pseudorandom sequence generated by the LFSR used is shown at Fig 4. This function is a set of repeating delta functions separated by the number of bits in the LFSR’s sequence length (2n-1).

Fig 4. Autocorrelation function.

Autocorrelation function of the pseudo-random number generator used in the experiments.

For operations that need un-correlated signals, we define different seeds for each LFSR block. On the other hand, for operations requiring correlated signals we employ the same LFSR output for all stochastic variables.

Generalization of stochastic computing

Different kinds of stochastic non-linear functions can be reproduced using stochastic correlated signals. In Fig 5a, we show an example of the implementation of the absolute value of a subtraction (|px-py|). Stochastic signals x and y are derived from binary numbers X and Y when comparing with a random number R, generated using one LFSR. The probability of getting x = '1' or y = '1' (values of px and py) is proportional to X and Y respectively. Since X and Y are correlated (they share the same LFSR) the probability of getting both signals x and y with a high level (x = y = '1') is min(px, py), while the probability of getting both signals with a low level (x = y = '0') is min(1- px, 1- py). These two situations provide a low signal at the XOR output. On the other hand, the probability of getting different values on x and y is equal to | px-py|. In this situation, the XOR output (z) provides a high value with probability pz = | px-py|.

Fig 5. Similarity estimation of X and Y (with correlated signals).

In contrast to the un-correlated scheme (case in which X and Y are compared with different random values), a non-linear function is obtained. The probability of the R signal of being between X and Y is proportional to |Px-Py| that is the switching activity of the output signal. (a) Probabilistic circuitry for the computation of |Px-Py|. (b) Experimental results when evaluating |Px-Py|.

The experimental measurements of this circuit confirm this behavior (see Fig 5b). In general, correlated signals evaluated through logic gates would implement max-min algebra functions. These types of functionalities are ideal for pattern recognition.

Data mining of huge databases

The probabilistic nature of stochastic logic is an advantage for the implementation of probabilistic-based pattern recognition methodologies [2] and pattern matching is in the core of many data mining processes. The purpose is to compare parameters (the features) from different objects with reference vectors that represent different categories. All the features define each object, thus configuring an m-dimensional vector (for m different features).

Fig 6 shows the stochastic circuit used to compare two m-dimensional vectors, providing at the output an estimation of the similarity (sjr) of both objects (vector 'r' in the database to mine, and the vector defining the category 'j' (xj)). A total of '2m' binary comparators and also 'm' randomly selected binary numbers (Rk for the k-th descriptor) are configured in parallel to create a total of 2m switching bits. Each pair of stochastic bits (xjk and rk) are compared through XNOR gates connected with a block performing the AND function between the m signals, thus providing at its output an estimation of the similarity (sjr) between the vector (r) and the category j (xj).

Fig 6. Stochastic architecture for comparing m-dimensional vectors.

A total of 'm' random signals are needed for each comparison.

Note the combination of both correlated and uncorrelated stochastic signals to obtain sjr (all the AND inputs must be uncorrelated between them so that we need 'm' random numbers Rk). This block would represent the typical implementation of a probabilistic processing unit combining both types of signaling. Therefore, the probabilistic signal obtained (sjr) can be expressed as: (1)

The switching activity at the output of the AND block is therefore proportional to the similarity between the object r and the category represented by vector xj. In Fig 7 we show the level curves associated to the Manhattan-based metric used in (1). As it is shown, the selected metric can be considered a good proximity estimator for fast mining of huge databases.

Fig 7. Level curves for the similarity metric used for the case of a two-dimensional space.

Hundreds of similarity estimators as the one shown in Fig 6 can be configured in parallel in a medium-sized FPGA, thus increasing considerably the mining speed in comparison with traditional processor-based techniques. Different vectors can be compared in parallel by using a Winner-Take All (WTA) architecture (see Fig 8). From 'n' different vectors of the database, the circuitry provides at its output the closer to reference vector r. Then, relative fraction of the area used by the LFSRs with respect to the total circuit area decreases with the number of vectors xj that can be compared.

Fig 8. Architecture used to estimate the closest category to the reference vector 'r'.

Each comparator (CP) provides at its output a switching signal proportional to the similarity of the two vectors connected to it. The Winner-Take All selects the highest frequency signal.

Thus, at the WTA output the category label 'j' that is activated is the one with the highest similarity: (2)

The WTA can be constructed by using binary counters (module-k) (see Fig 9). Only a maximum number of clock cycles per comparison (N>k) are allowed, and therefore the number of cycles needed to overflow fixes a minimum similarity value to be distinguished smin so that N•smin = k. If all the similarities at the input of the WTA (sjr) are lower than smin, the most probable scenario is not to obtain any positive result at the output of the WTA.

Fig 9. Winner-Take All.

Architecture used to select the category associated to the sir with the higher activity.

For any similarity value sjr>smin between two vectors (xj and r) we have that the probability of identifying vector r as belonging to class 'j' is close to 1. In Fig 10 we show the relationship between the distances of two vectors (|x-y|) and the number of cycles (N) needed to obtain an overflow of one of the counters of the WTA. We compare the analytical formula of the theoretical behavior (solid line showing the formula N = k/s, where the similarity is s = 1-d since we only vary the distance in one dimension) and the measurements obtained with an ALTERA Cyclone III FPGA (dashed lines). As it can be seen, a close relationship is obtained between the expected behavior and the probabilistic implementation.

Fig 10. Relationship of distance between vectors (|x-y|) and the number of cycles needed to overflow a module-k counter of the WTA.

The switching activity of the similarity stochastic signal (sjr) is dependent on the distance (s = 1-d). As the higher is the distance, lower will be the switching activity of s, and higher the number of cycles needed to overflow the counter.

The probability of identifying vector r as belonging to class 'j' can be estimated considering the probability of obtaining more than 'k' HIGH values from a signal with switching activity 'sjr' when waiting a total of N cycles (N>k). (3) where N is the total number of cycles used to compare any set of vectors. Function (3) is represented in Fig 11 for the special case in which k = 16 and N = 80 as a function of the similarity sjr. For this case, smin = k/N = 0.2, note that Pjr>0.5 for sjr>smin in Fig 11.

Fig 11. Variation of query identification probability with respect to similarity.

An arbitrary threshold of smin = 0.2 is selected.

The similarity search function (1) has the inherent advantage of its analytical simplicity. In this sense, given two randomly selected m-dimensional vectors xj and r, it is possible to compute the cumulative distribution function of obtaining a similarity lower than a given selected value 'z'. From basic probability theory we have that this cumulative distribution function can be estimated as: (4) where Fz(z) is the probability of obtaining a similarity lower than z, Ω is the volume for which g(y)<z, being g(y) the function under consideration (1). Vector y is composed by all the parameters for which 'g' is dependent (the components of both xj and r vectors that are stochastic signals with values between 0 and 1).

After some algebra, and considering (1) for the estimation of Ω and that each parameter is bounded between 0 and 1 we have: (5) where 'm' is the dimension of the vectors. If we want to estimate the probability density function fz(z), we have to derive the cumulative distribution function with respect the selected similarity (z), fz(z) = zFz(z)

From expression (5) we can estimate the number of positive identifications from a database with W unknown objects if we select a minimum similarity of smin (Positives≈W•(1-F(smin))). Expression (5) is also very useful to estimate the minimum database width Wmin needed to identify an object with a specific property (assuming a random database): (6)


We implemented the proposed methodology in a FPGA-based PCIe board (ProcStarIV110E-4B) fabricated by GIDEL Ltd (see Fig 12). This board incorporates four ALTERA STRATIX III 110E FPGAs along with 32GB of DDR2 SODIMM memory banks to allocate the database. The communication with the board is done through a PCI-express connector of a PC. The clock frequency of operation of the board is 87.5 MHz and the maximum power dissipation is about 100W.

Fig 12. FPGA-based PCIe board used for the data mining process from GIDEL.

Inside the four FPGA cores we configured a total of 400 similarity comparators per core (design shown in Fig 6), thus implementing a total of 1600 stochastic comparators in the board that operate in parallel. The dimension of the vectors are selected to be m = 12. To test the circuit effectiveness, we created a similarity search in a random database with a total of 2.56106 12-D vectors setting an arbitrary minimum similarity of smin = 0.2. The final positive results are shown in Fig 13, where we show, as a function of the similarity 's': the number of vectors of the database with this similarity (dark diamonds), the number of vectors that are finally selected by the circuitry as belonging to class 'c' (circles), the ideal number of vectors to be selected (solid line) and the expected number of vectors to be selected (dashed line fz(sjr)Pjr). It is visible that the experimental and theoretical results (circles and dashed line) are similar. We also can see that nearly all the vectors with a similarity higher than the selected threshold are identified (circles and solid line). Unfortunately there is a non-negligible fraction of vectors with a similarity lower than smin that are selected (circles with s<smin = 0.2). This fact is due to the non-negligible tie of Pjr(sjr) for sjr<smin and the exponential dependence of fz(sjr) with sjr.

Fig 13. Number of positive identifications from a database with 2.56106 particles when smin = 0.2.

The solution to erase such false positives is to filter-out the final results using software. The impact of this filtering on the database screening timing is small since only a low volume of vectors must be recomputed (rather than the billions of vectors that the database can contain).

The total number of positives to be selected by the stochastic circuitry can be estimated numerically as: (7)

Expression (7) can be used to select an optimal number for k and N to control the number of positive results within reasonable values. In Fig 14 we show the different types of negatives and positives provided by the system. False positives are those positives with a similarity lower than the threshold smin. False negatives are those vectors that, even presenting a higher similarity than smin, are not found by the system. Finally, the true positives and negatives are those vectors that are correctly classified by the system.

Fig 14. For each computation, a given number of false positives and negatives are provided by the probabilistic system.

False positives can be filtered out by software while false negatives are lost.

Although the false positives can be filtered-out by software, the vectors with s>smin that are finally not found by the hardware (false negatives) cannot be recovered (in Fig 13, the false negatives are the difference between the solid line and the circles). In this sense, the precision of the system is defined as the ratio between true positives and the total number of possible positives (true positives + false negatives).


In Table 1 we show the dependence of η with respect to parameter k assuming fz = 1 with smin = 0.5. Increasing k increases the efficiency but decreases the screening speed of the circuit. In Table 1 we find the relationship between η and the number of cycles needed to overflow a WTA counter (k value). For most applications, a value of k = 8 provides good results in terms of accuracy and computation time.

Table 1. Relationship between the k value and the precision of the system when fz = 1 and using smin = 0.5 (i.e. N = 2k).

The circuit speed can be estimated from the time needed to screen a database (tp) with a total of W vectors: (9) where W is the number of vectors in the database, fRAM is the data transfer frequency of the DDR-DRAM (of the order of 16GB/s for the PCIe used in the experiment), Tclk is the global clock period of the circuit (operating at 87.5MHz), 'size' is the number of bytes per vector (12 bytes in our case), 1600 is the number of stochastic comparators implemented inside the FPGAs, parameter N is the number of clocks used by the WTA to process the inputs (signals sjr), and finally tsetup is the setup time needed for board initialization (of the order of 1ms). Finally, the speed of the screening process is estimated as a function of the number of vectors in the database (fproc = W/tp). In Fig 15 we show the screening speed (in millions of comparisons per second) as a function of the database width (in millions of vectors), where the vectors are composed by 12 bytes (m = 12). The low speed obtained by the proposed methodology when the database is small is basically due to the fixed setup time of the system.

Fig 15. Data mining speed variation with database width when using probabilistic computing.

Each comparison implies the processing of two vectors of 12 Bytes each one.

We compared a conventional implementation using binary-based digital hardware of a multi-vector comparator implementing the metric proposed in (1) with the proposed probabilistic system. In Fig 16 we show the conventional implementation of expression (1). Finally, in Fig 17 we compare the result of such similarity comparators with the minimum similarity smin. In Table 2 we compare both implementations in terms of FPGA resources when using an ALTERA Cyclone III EP3C25F324C8 device. The vectors to be compared are fixed to 12 dimensions. As it can be seen, the ratio of Logic Elements needed by the conventional and the probabilistic implementation increases as the number of vectors to be compared grows. The area ratio between both systems is of the order of 56 while the ratio in terms of circuit speed is constant and about 1/k (1/8). Therefore, the probabilistic implementation speeds up the screening process of the database by a factor of 7 when compared to a conventional digital implementation using the same hardware area. In other words, the total performance when using the proposed architecture is higher by a factor of 7. It means that, the lack in speed can be compensated by using less hardware area.

Fig 16. Conventional implementation of a m-dimensional digital comparator where expression (1) is implemented.

The first comparator and the multiplexer implement the absolute value function while the multiplier provide the product for all the dimensions.

Fig 17. Global comparator between the reference vector and each vector in the database.

Block CP is implementing the circuitry shown in Fig 16. The result is compared with smin.

Table 2. Relationship between conventional and probabilistic implementation in terms of FPGA resources for an Altera Cyclone III device.

To sum up, the presented application is an example where stochastic computing is advantageous over conventional computing systems. Since the presented implementation takes advantage of the high parallelism of stochastic computing, less circuit area implies a lower power demand to achieve a determined processing speed. The source code of both implementations (conventional and probabilistic) can be found in S1 Appendix.


We have presented a new and unconventional computing technique for ultra-fast mining of huge databases. The methodology is based on the use of probabilistic pulsed signals. We describe how correlated bit streams can be used to implement non-linear functions like the absolute value function, which have been also developed by other research group [31]. In the final architecture we allow the use of both correlated and uncorrelated stochastic bit streams. The combination of both types of switching signals increases the mathematical capacity of original stochastic computing implementations. The data to be mined is translated to spikes and processed by a simple digital circuitry. The simplicity of the circuitry is used to implement hundreds of stochastic comparators inside Field-Programmable Gate Arrays and oriented to screen huge databases. The final implementation uses an FPGA-based PCIe board for the screening. This implementation uses less hardware resources than conventional digital methodologies (based on binary and not probabilistic logic) and is able to process the order of 13GBytes of information per second (in contrast to the estimated 2GBytes/s of speed that could be achieved by the conventional implementation using the same hardware area). With the 12-dimensional space used to allocate each vector in the example shown in this paper we obtain the order of 1 billion of comparisons per second. A patent application has been done for this new mining methodology [32].

Supporting Information

Author Contributions

Conceived and designed the experiments: AM VC AO MLA JLR. Performed the experiments: AM. Analyzed the data: JLR VC. Wrote the paper: JLR.


  1. 1. Szalay A, Gray J. (2006) 2020 computing: Science in an exponential world. Nature 440: 413–414. pmid:16554783
  2. 2. Bishop C.M., editor. (2006) Pattern recognition and machine learning. New York: Springer.
  3. 3. Fayos J, Fayos C. (2007) Wind data mining by Kohonen neural networks. PLoS ONE 2.
  4. 4. Stegmayer G, Gerard M, Milone D. (2012) Data mining over biological datasets: An integrated approach based on computational intelligence. IEEE Computational Intelligence Magazine 7: 22–34.
  5. 5. Zou J, Han Y, So S-. (2008) Overview of artificial neural networks. Methods in Molecular Biology 458: 15–23. pmid:19065803
  6. 6. Ballester PJ, Richards WG. (2007) Ultrafast shape recognition for similarity search in molecular databases. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463: 1307–1321.
  7. 7. Pan F, Wang W, Tung AKH, Yang J. (2005) Finding representative set from massive data. Proceedings—IEEE International Conference on Data Mining, ICDM: 338–345.
  8. 8. Gaines BR. (1975) Stochastic and fuzzy logics. Electron Lett 11: 188–189.
  9. 9. Gaines R. (1969) Stochastic Computing Systems. Advances in Information Systems Science. Tou ,ed., 2:37–172.
  10. 10. Kim Y, Shanblatt MA. (1995) Random noise effects in pulse-mode digital multilayer neural networks. IEEE Trans Neural Networks 6: 220–229.
  11. 11. Canals V, Morro A, Rosselló JL. (2010) Stochastic-based pattern-recognition analysis. Pattern Recog Lett 31: 2353–2356.
  12. 12. Hori M, Ueda M, Iwata A. (2006) Stochastic computing chip for measurement of manhattan distance. Japanese Journal of Applied Physics, Part 1: Regular Papers and Short Notes and Review Papers 45: 3301–3306.
  13. 13. Rosselló JL, Canals V, Morro A. (2012) Probabilistic-based neural network implementation. Proceedings of the International Joint Conference on Neural Networks.
  14. 14. Li P., Lilja D.J., Qian W., Bazargan K., Riedel M.D. (2014) Computation on stochastic bit streams digital image processing case studies. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 22: 449–462.
  15. 15. Aggarwal CC, Han J, Wang J, Yu PS. (2005) On high dimensional projected clustering of data streams. Data Mining and Knowledge Discovery 10: 251–273.
  16. 16. Bayardo RJ, Ma Y, Srikant R. (2007) Scaling up all pairs similarity search. 16th International World Wide Web Conference, WWW2007: 131–140.
  17. 17. Gullo F, Ponti G, Tagarelli A, Greco S. (2009) A time series representation model for accurate and fast similarity detection. Pattern Recognit 42: 2998–3014.
  18. 18. Kontos D, Megalooikonomou V. (2005) Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data. Pattern Recognit 38: 1831–1846. pmid:16565747
  19. 19. Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu et al. (2012) Searching and mining trillions of time series subsequences under dynamic time warping. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 262–270.
  20. 20. Zhen Y, Yeung D-. (2013) Active hashing and its application to image and text retrieval. Data Mining and Knowledge Discovery 26: 255–274.
  21. 21. Koren Y. (2008) Factorization meets the neighborhood: A multifaceted collaborative filtering model. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 426–434.
  22. 22. Wang X, Smith K, Hyndman R. (2006) Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery 13: 335–364.
  23. 23. Li H, Guo C. (2012) Similarity search for time series based on efficient warping measure. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  24. 24. Kaznadzey A, Alexandrova N, Novichkov V, Kaznadzey D. (2013) PSimScan: Algorithm and utility for fast protein similarity search. PLoS ONE 8.
  25. 25. Zhu Q, Wang X, Keogh E, Lee S-. (2011) An efficient and effective similarity measure to enable data mining of petroglyphs. Data Mining and Knowledge Discovery 23: 91–127.
  26. 26. Grossman R, Gu Y. (2008) Data mining using high performance data clouds: Experimental studies using sector and sphere. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 920–927.
  27. 27. Zhou P, Lei J, Ye W. (2011) Large-scale data sets clustering based on MapReduce and hadoop. Journal of Computational Information Systems 7: 5956–5963.
  28. 28. Kitsos P, Sklavos N, Zewas N, Koufopavlou O. (2001) A reconfigurable linear feedback shift register (LFSR) for the bluetooth system. Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems 2: 991–994.
  29. 29. Peterson W. (1961) Error Correcting Codes. Jonh Wiley and Sons.
  30. 30. Korn G. (1966) Random-Process Simulation and Measurements. McGraw-Hill Book Co.
  31. 31. Alaghi A, Hayes JP. (2013) Exploiting correlation in stochastic circuit design. 2013 IEEE 31st International Conference on Computer Design, ICCD 2013: 39–46.
  32. 32. Rosselló JL, Morro A, Canals V. (2012) Sistema digital probabilístico para la exploración y reconocimiento de formas eficiente en grandes bases de datos.