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Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis
شتاب دهنده سخت افزاری بهینه سازی شده برای برنامه های استخراج داده بر روی چهارچوب های embedded: مطالعه موردی تجزیه و تحلیل مؤلفه اصلی-2019 With the proliferation of mobile, handheld, and embedded devices, many applications such as data min- ing applications have found their way into these devices. However, mobile devices have stringent area and power limitations, high speed-performance, reduced cost, and time-to-market requirements. Furthermore, applications running on mobile devices are becoming more complex requiring high processing power. These design constraints pose serious challenges to the embedded system designers. In order to pro- cess the applications on mobile and embedded systems, effectively and efficiently, optimized hardware architectures are needed. We are investigating the utilization of FPGA-based customized hardware to ac- celerate embedded data mining applications including handwritten analysis and facial recognition. For these biometric applications, Principal Component Analysis (PCA) is applied initially, followed by similar- ity measure. In this research work, we introduce novel and efficient embedded hardware architectures to accelerate the PCA computation. PCA is a classic technique to reduce the dimensionality of data by transforming the original data set into a new set of variables called Principal Components (PCs) that rep- resent the key features of the data. We propose two hardware versions for PCA computation, each with its unique optimization techniques to enhance the performance of our designs, and one specifically with additional techniques to reduce the memory access latency of embedded platforms. To the best of our knowledge, we could not find similar work for PCA, specifically catered to the embedded devices, in the published literature. We perform experiments to evaluate the feasibility and efficiency of our designs us- ing a benchmark dataset for biometrics. Our embedded hardware designs are generic, parameterized, and scalable; and achieve 78 times speedup as compared to its software counterparts Keywords: Data mining | Dimensionality reduction techniques | Embedded and mobile systems | FPGAs | Hardware acceleration | Principal Component Analysis |
مقاله انگلیسی |
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Micro Sequence Identification of DNA Data Using Pattern Mining Techniques
شناسایی میکروارگانیسم داده های DNA با استفاده از تکنیک های کاوش الگو-2018 A rapid development of NGS(Next generation Sequencing) technologies can be able to produce large amounts of sequence
data,which is leading in to a wide range of new applications. Sequence matches between a translated nucleotide sequence and a
well known biological protein can able to provide a strong evidence for the presence of homologous coding region, and such
similarities often be identified even between a distantly related genes. Common techniques often restrict indels in the alignment
to improve time,speed, whereas more Flexible aligners are too slow for large-scale applications. Moreover, many current aligners
are becoming inefficient as generated reads grow ever larger. To perform such high dimensional process, it requires a special
hardware implementation & designing, such implementation can also increases a complexity of efficiency and hardware. The
Field Programmable Gate Array is the well-known to design and we propose a high efficient algorithm for sequence detection in
any of bioinformatics data. Unlike previous methods, the proposed pattern matching algorithm can identifies the sequence of
each factor on the basis of their occurrences. This method can computes the multi-level similarity measure with an available
sequences. Based on the multi-level sequence similarity measure computed a single sequence of bioinformatics data can be
identified. The proposed method produces efficient result in sequence searching and detection and improves the hardware
utilization which in terms reduces the time complexity as well.
Keywords: Bioinformatics; Pattern Matching; Sequence Identification; Dynamic Programming; Hardware Acceleration |
مقاله انگلیسی |
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Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations
Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations-2015 Rationale and Objectives:Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins duringCT-guided liver tumor ablations. Although both accuracy and speed can
be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially.
To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique on the basis of volume subdivision with hardware acceleration using a graphics processing unit (GPU). We compared the registration accuracy and processing time of
GPU-accelerated volume subdivision–based nonrigid registration technique to the conventional nonrigid B-spline registration technique.
Materials and Methods:Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volumesubdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice similarity coefficient [DSC] and 95%
Hausdorff distance [HD]) and total processing time including contouring of ROIs and computation were compared using a paired Studentttest.
Results:Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.33.7% versus 89.34.9%
(P= .41) for DSC and 13.15.2 versus 11.46.3 mm (P= .15) for HD. Total processing time of the GPU-accelerated registration and
B-spline registration techniques was 8814 versus 557116 seconds (P< .000000002), respectively; there was no significant difference
in computation time despite the difference in the complexity of the algorithms (P= .71).
Conclusions:The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly
less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into
routine clinical practice.
Key Words:Nonrigid image registration; GPU-accelerated image processing; B-spline; mutual information. |
مقاله انگلیسی |