Precision medicine and FPGA technology: Challenges and opportunities

Hill, Brian; Smith, Jaclyn; Srinivasa, Gans; Sönmez, Kemal; Sirasao, Ashish; Gupta, Amit Kumar; Mukherjee, M.

doi:10.1109/mwscas.2017.8053008

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…It identifies regions of similarity which may be a consequence of functional, structural and evolutionary relationships between the sequences. It is mainly applied in precision medicine, screening for the newborn and screening for carriers of diseasecausing mutations [1,2,3,4,5], etc. The Pair Hidden Markov Model (Pair-HMM) has various inference algorithms such as optimal sequence alignment (Viterbi algorithm [6]) and the overall alignment probability (forward algorithm [7]).…”

Section: Introductionmentioning

confidence: 99%

Pair-HMM accelerator based on non-cooperative structure

Wang

Lei

Dou

2019

IEICE Electron. Express

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Pair Hidden Markov Model (Pair-HMM) forward algorithm is gaining increasing popularity in biological research tools. We propose a novel non-cooperative structure of Pair-HMM forward algorithm accelerator on Field Programmable Gate Array (FPGA). We employ a tasklevel parallel scheme in the structure. We design the non-cooperative Processing Element (PE) to complete Pair-HMM forward algorithm independently. Our three-layer tree topology improves the scalability for different FPGAs. Compared to previous works, our structure reduces the idle cycles which occurs in the systolic array structure and the PE ring structure. Compared with the PE ring, our implementation on Arria 10 can achieve 1.19× speedups.

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Section: Introductionmentioning

confidence: 99%

Pair-HMM accelerator based on non-cooperative structure

Wang

Lei

Dou

2019

IEICE Electron. Express

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“…Gene sequence alignment is a process that compares the target sequence of DNA, RNA, or a protein with the reference sequence and obtains the similarity between the two gene sequences [1]. This technology is widely applied in the process of screening for disease-causing mutation carriers [2][3][4], early-stage cancer detection [5,6], etc. In a typical gene alignment problem [7], the Pair Hidden Markov Model (Pair-HMM) is most commonly used to evaluate the overall likelihood of any possible alignments between two sequences.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of FPGA-Based Pair-HMM Accelerator Structures

2019

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As one of the most important and computationally-intensive parts in bioinformatics analysis, the Pair Hidden Markov Model (Pair-HMM) forward algorithm is widely recognized and has great potential. Therefore, it is important to accelerate the process of this algorithm. There are various approaches to accelerate Pair-HMM, especially the accelerators stemmed from the Field Programmable Gate Array (FPGA) due to the highly-customizable on-chip resources and deep pipeline potential available to the designer. In this paper, we focus on the FPGA-based accelerators for the Pair-HMM forward algorithm proposed in recent years. The non-cooperation structure, which was proposed in our previous work, is compared with the Systolic Array (SA) structure and PE ring structure in the structure characteristics, calculation mode, computational efficiency, and storage requirement. We build an analysis model to evaluate the performance of the ring structure and our non-cooperative structure. Furthermore, based on this, we provide a detailed analysis of the characteristics of structures of different accelerators and of the selection of a suitable structure for different scenarios. Based on the non-cooperative PE structure, we design a new chain topology for the accelerator. Experimental results show that our non-cooperation structure is superior to the other structures in performance and execution efficiency, and our new topology improves the performance of the accelerator. Finally, we propose some ideas about the improvement of the non-cooperative structure accelerator for future work.

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