Pair-HMM accelerator based on non-cooperative structure

Wang, Pengfei; Lei, Yuanwu; Dou, Yong

doi:10.1587/elex.16.20190402

Cited by 2 publications

(14 citation statements)

References 28 publications

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“…3. Non-cooperative structure: We proposed the non-cooperative PE structure in our previous work [20]. Different from the two mentioned structures, our non-cooperative PE structure has no cooperation among the PEs.…”

Section: Structures' Characteristicsmentioning

confidence: 99%

“…The execution of the gene sequence alignment needs intensive floating-point operations. Therefore, parallel approaches such as multi-core Central Processing Units (CPUs) [11,12], Graphics Processing Units (GPUs) [13,14], and Field Programmable Gate Arrays (FPGAs) [15][16][17][18][19][20] are used to improve the calculation speed. Compared with CPU and GPU, FPGA has sufficient computational and storage resources and can customize the hardware according to the requirements, especially for computationally-intensive applications.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the challenges of a large number of alignment programs and different sizes of sequences put forward different methods to solve this problem. Considering the need for throughput and performance while dealing with gene sequences with variable sizes, we designed a Pair-HMM forward algorithm accelerator with the non-cooperative PE structure in [20]. In this structure, every PE supplies high computational efficiency for the alignment tasks of gene sequences with different sizes, independently.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, while implementing this optimized algorithm, the cost of the memory resources will increase accordingly. On this basis, the optimized Pair-HMM forward algorithm used in [20] is shown in Algorithm 2 and the following. The Read sequence, the Haplotype sequence, and quality factor sequences are the inputs of the Pair-HMM forward algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…To illustrate the comparative analysis of FPGA-based accelerators, we use two concepts defined in [20]. For further analysis, we show the characteristics of the algorithm as Figure 2 and the following.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Structures' Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of FPGA-Based Pair-HMM Accelerator Structures

2019

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Hardware accelerator for high accuracy sign language recognition with residual network based on FPGAs

Yang,

Li,

Hao

et al. 2024

IEICE Electron. Express

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The ResNet series of networks has demonstrated powerful capabilities in the fields of object detection and image classification, garnering increasing attention from researchers. However, due to their deep network architectures, accelerator development based on FPGA faces challenges associated with limited on-chip resources and lengthy design cycles. This paper presents a versatile hardware acceleration system based on FPGA, achieving optimization through both hardware implementation and software inference architecture. The system reduces network complexity by employing techniques such as inter-layer fusion and dynamic quantization, while enhancing hardware resource utilization through channel parallelism and tightly-pipelined hardware design principles. By configuring and reusing computation units, the forward inference process of ResNet series networks can be rapidly deployed on FPGA, shortening the development and validation cycles. The proposed system is validated using the ResNet-18 model on a PYNQ-Z2 development board within a gesture recognition application scenario. The overall power consumption of the system is 2.136W, with hardware inference accuracy reaching 98.87%.

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Pair-HMM accelerator based on non-cooperative structure

Cited by 2 publications

References 28 publications

Comparative Analysis of FPGA-Based Pair-HMM Accelerator Structures

Comparative Analysis of FPGA-Based Pair-HMM Accelerator Structures

Hardware accelerator for high accuracy sign language recognition with residual network based on FPGAs

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