GPU-Accelerated HMM for Speech Recognition

Yu, Liang; Ukidave, Yash; Kaeli, David

doi:10.1109/icppw.2014.59

Cited by 12 publications

(7 citation statements)

References 14 publications

(10 reference statements)

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“…For example, [31], proposes a new distributed multidimensional HMM (DHMM) for multi-object trajectory interaction modeling, the results show superior performance and greater accuracy of the proposed distributed 2D HMM. In [32], the authors present a parallelized HMM to accelerate isolated words speech recognition. Another work of [33] presents a GPU implementation in which they proposed a C and Cuda implementation for the forward, Viterbi and BW algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Since both the serial forward and the proposed parallel version in each paper were executed using the same dataset with the same parameters, we compute the relative speedup between the two in each case and compare it over the other versions. Table IV shows the result of average relative speedup comparison of ParaDist-Forward algorithm compared to those of [32], [33], [34], [35], [36], [37] and [38]. The results show that the speedup of the proposed model has the best results compare to the benchmark models.…”

Section: B Speedupmentioning

confidence: 99%

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ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark

Sassi¹,

Anter²,

Bekkhoucha³

2021

IJACSA

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Big Data is an extremely massive amount of heterogeneous and multisource data which often requires fast processing and real time analysis. Solving big data analytics problems needs powerful platforms to handle this enormous mass of data and efficient machine learning algorithms to allow the use of big data full potential. Hidden Markov models are statistical models, rich and widely used in various fields especially for time varying data sequences modeling and analysis. They owe their success to the existence of many efficient and reliable algorithms. In this paper, we present ParaDist-HMM, a parallel distributed implementation of hidden Markov model for modeling and solving big data analytics problems. We describe the development and the implementation of the improved algorithms and we propose a Spark-based approach consisting in a parallel distributed big data architecture in cloud computing environment, to put the proposed algorithms into practice. We evaluated the model on synthetic and real financial data in terms of running time, speedup and prediction quality which is measured by using the accuracy and the root mean square error. Experimental results demonstrate that ParaDist-HMM algorithms outperforms other implementations of hidden Markov models in terms of processing speed, accuracy and therefore in efficiency and effectiveness.

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Section: Related Workmentioning

confidence: 99%

Section: B Speedupmentioning

confidence: 99%

ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark

Sassi¹,

Anter²,

Bekkhoucha³

2021

IJACSA

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show abstract

“…HMM computation in double precision can be treated as numerically stable. However, in acceleration systems (also GPUs as in [31][32]), where single and half-precision computations are widely used, the length constraints have to be applied to the observation sequence in order to ensure numerical stability.…”

Section: Remarkmentioning

confidence: 99%

FPGA implementation of logarithmic versions of Baum-Welch and Viterbi algorithms for reduced precision hidden Markov models

Pietras¹,

Klęsk²

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. This paper presents a programmable system-on-chip implementation to be used for acceleration of computations within hidden Markov models. The high level synthesis (HLS) and "divide-and-conquer" approaches are presented for parallelization of Baum-Welch and Viterbi algorithms. To avoid arithmetic underflows, all computations are performed within the logarithmic space. Additionally, in order to carry out computations efficiently -i.e. directly in an FPGA system or a processor cache -we postulate to reduce the floating-point representations of HMMs. We state and prove a lemma about the length of numerically unsafe sequences for such reduced precision models. Finally, special attention is devoted to the design of a multiple logarithm and exponent approximation unit (MLEAU). Using associative mapping, this unit allows for simultaneous conversions of multiple values and thereby compensates for computational efforts of logarithmic-space operations. Design evaluation reveals absolute stall delay occurring by multiple hardware conversions to logarithms and to exponents, and furthermore the experiments evaluation reveals HMMs computation boundaries related to their probabilities and floating-point representation. The performance differences at each stage of computation are summarized in performance comparison between hardware acceleration using MLEAU and typical software implementation on an ARM or Intel processor. to the network, and as the operation of the network is affected by external factors, real time computation is not guaranteed. Hence, for low latency applications, full HMM processing and computation within a dedicated embedded system is a reliable choice, and this has been shown for diverse systems such as speech recognition [11][12], pattern detection [13] and for AAV/ AUV [14]. Unfortunately, the increasing complexity of HMMs is paralleled by the growing demand for computational resources, especially memory and data throughput. Hence, when considering the hardware acceleration of HMM algorithms (forward-backward, Viterbi, Baum-Welch), the size of the HMM has to be minimized while the stability of numerical calculation still has to be guaranteed. As mentioned in [15], typical calculations associated with HMM rapidly exhaust the precision of numerical representation. This is related to the necessity of performing long sequences of multiplications of probability values (which are close to zero) for state transitions or observation emissions. Therefore, key algorithms are computed within the logarithmic space instead of applying some scaling factors [16]. Decreasing the size of an HMM can be immediately achieved by reducing the precision of its numerical representation (transition and emission matrices), e.g. down to: 32 bits (single), 16 bits (half) or 8 bits (quarter). This action directly affects and restricts the maximum length of the observation sequences examined, which means that computations on longer sequences may become numerically unstable.

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“…The efficiency is noticeable for large number of states and iterations. Yu et al [29] achieved 9.2x and 7.9x speedup during the training and testing stages, when used as a Speech Recognition platform for real-time applications. The performance can be limited due to the hardware's memory bandwidth and availability of resources.…”

Section: Related Workmentioning

confidence: 99%

“…The performance can be limited due to the hardware's memory bandwidth and availability of resources. Yu et al [29] states that the GPU version outperforms a single threaded CPU version for internal states greater than 256 for the Forward Algorithm.…”

Section: Related Workmentioning

confidence: 99%

Cryptanalysis of Classic Ciphers Using Hidden Markov Models

Vobbilisetty

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Cryptanalysis of Classic Ciphers Using Hidden Markov Models by Rohit Vobbilisetty Cryptanalysis is the study of identifying weaknesses in the implementation of cryptographic algorithms. This process would improve the complexity of such algorithms, making the system secure. In this research, we apply Hidden Markov Models (HMMs) to classic cryptanalysis problems. We show that with sufficient ciphertext, an HMM can be used to break a simple substitution cipher. We also show that when limited ciphertext is available, using multiple random restarts for the HMM increases our chance of successful decryption.

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GPU-Accelerated HMM for Speech Recognition

Cited by 12 publications

References 14 publications

ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark

ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark

FPGA implementation of logarithmic versions of Baum-Welch and Viterbi algorithms for reduced precision hidden Markov models

Cryptanalysis of Classic Ciphers Using Hidden Markov Models

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