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FPGA GPU implementation

3. FPGA Implementation The FPGA implementation is shown in Figure 1; it con-sists of three blocks: (a) distortion removal and rectification ( cf. Section3.1), (b) disparity calculators ( 3.2) and (c) predecessor array and backtrack module (cf. Section 4.5). 3.1. Distortion removal and rectificatio FPGA implementation of a GPU. Contribute to RedTopper/FPGA-GPU development by creating an account on GitHub Furthermore, FPGA implementation is presented and its performance running on chips from two major manufacturers are reported. A comparison of GPU and FPGA implementations is quantified and ranked. It is reported that FPGA design delivers the best performance for such a task while GPU is a strong competitor requiring less development effort with superior scalability

framework idea is implemented using an Nvidia TX2 GPU and a Xilinx Artix-7 FPGA. Experimental results indicate that the proposed framework can achieve faster computation and much lower power consumption. Keywords-FPGA, GPU, Heterogeneous Computing, Low-Powered Devices I. INTRODUCTION Deep learning has seen many industrial successes over the past decade to many practical problems, including im GPU Flexibility FPGA lacks flexibility to modify the hardware implementation of the synthesized code, being a no-problem issue for GPUs developers. GPU Size FPGA's lower power consumption requires less thermal dissipation countermeasures, implementing the solution in smaller dimensions. FPGA FPGAs are hardware implementations of algorithms, and since a hardware implementation usually operates faster than a software implementation, they perform very well. Unlike FPGAs, GPUs execute software; performing complex algorithms takes many sequential GPU instructions compared to an FPGA's hardware implementation and a Field Programmable Gate Array (FPGA) to test the design. Also this design can be used as part of a full GPU implementation. The motivation to make this implementation is to have a fully functional rasterizer to study what is the performance achieved with the specialized way for small triangles. implementation. 1.2Actor Furthermore, FPGAs can implement custom data types whereas GPUs are limited by architecture. With neural networks transforming in many ways and reaching out to more industries, it is useful to have the adapatability FPGAs offer. Now You Must Be Wondering, What Are FPGAs? An FPGA (Field Programmable Gate Array) is a customisable hardware device

A. GPU Implementation GPUs are Single Instruction Multiple Data (SIMD) com-puting devices. Parallelizable tasks are executed on the GPU as kernels, which can be considered as arrays of threads that operate on different sets of data. Threads are organized into blocks, and many blocks can be launched in a single kernel execution In the FPGA implementation, we designed the task partition and memory hierarchy according to the analysis of datasets scale and their access pattern. In the GPU implementation, we designed a fast.. We then advanced the implementation through FPGA synthesis, logic placement and routing to get the BIN files to configure the HAPS system. This implementation of the Imagination PowerVR GX6250 GPU core ran at 7.3MHz. Adding live video outpu the embedded filter implementation on the FPGA. Section 3 details the GPU filter implementation. Section 4 explains the measurement setup for both implementations. Section 5 presents the results in terms of Energy, performance, and accuracy. Finally, Section 6 summarizes the paper. 2. 2D FIR FILTER SYSTEM ON THE FPGA We consider a 2D separable filtering implementation that i

GitHub - RedTopper/FPGA-GPU: FPGA implementation of a GP

FPGA implementation and optimizations, Section IV describes the BLAS level 2 implementation using novel banked memory systems, Section V presents the experimental setup, Section VI provides the evaluation of BLAS level 2 implementations on the FPGA, CPU and GPU platforms. Section VII concludes the paper. II. RELATED WOR In the FPGA implementation, we designed the task partition and memory hierarchy according to the analysis of datasets scale and their access pattern. In the GPU implementation, we designed a fast and scalable SpMV routine with three passes, using a modified Compressed Sparse Row format GPUs are built for parallel calculations (many parallel ALUs) and fast memory access. FPGAs consist of an array of logic gates that can perform any digital implementation desired by the developer. FPGA can be a networking switch, a CPU or a bitcoin miner. FPGA offers the maximin possible flexibility to the digital engineer

FPGA, GPU, and CPU implementations of Jacobi algorithm for

How to get started on designing a GPU on an FPGA - Quor

Compared to FPGAs and GPUs, the architecture of CPUs has a limited number of cores optimized for sequential serial processing. Arm® processors can be an exception to this because of their robust implementation of Single Instruction Multiple Data (SIMD) architecture, which allows for simultaneous operation on multiple data points, but their performance is still not comparable to GPUs or FPGAs Fpga, Low Prices. Free UK Delivery on Eligible Order To implement the GPU may not be enough resources in the FPGA. Here you need a strong type Intel Arria and Stratix or Xilinx Virtex. But for simple graphic implementations will be sufficient and available. On opencores.org there are ready solutions. Can study how they are implemented. But first you need to learn the basics for graphical output

FPGA vs. GPU for Deep Learning Applications - Inte

on FPGAs are compiled to custom processing pipelines built up from the programmable resources on the FPGA (for example ALMS, DSP, and memory blocks). By focusing hardware resources only on the algorithm to be executed, FPGAs can provide better performance per watt than GPUs for certain applications tween GPUs and FPGAs, as well as a migration path from GPUs to ASIC implementations. The remainder of the paper is structured as follows. Sec-tion 2 describes the FPGA's PCIe implementation. Section 4 describes the mechanism that enables direct GPU-FPGA transfers. Section 4 describes our experimental methodol-ogy

Mining on the new Nvidia RTX 2060 GPU - Crazy-Mining

Using GPUDirect the FPGA has direct access to the mapped GPU RAM. Fig. 1: Direct transfer without CPU involvement. XDMA Implementation from Xilinx This implementation is based on the XDMA IP from Xilinx. With this IP the host can initialize any DMA transfer between the FPGA internal address space and the I/O-memory address space By comparison, CPUs may need to execute thousands of instructions to perform the same function that an FPGA maybe able to implement in just a few cycles. All of this, of course, is part of a much larger discussion on the relative merits of FPGAs and GPUs in deep learning applications — just like with turbo kits vs. superchargers Algorithm Acceleration using GPUs, FPGAs and Custom ICs. Over the last few years, Graphics Processing Units (GPUs) have become increasingly flexible and powerful. GPUs are used to drive the display of desktop and laptop computers. Recent GPUs consist of a multitude (up to 240) of simple processors, which operate in lock-step A couple of FPGAs in mid-air (probably) Connectivity. On an FPGA, you can hook up any data source, such as a network interface or sensor, directly to the pins of the chip.This in sharp contrast to GPUs and CPUs, where you have to connect your source via the standardized buses (such as USB or PCIe) — and depend on the operating system to deliver the data to your application bedded FPGA substantially outperforms a GPU implementation in terms of energy efficiency and execution time. However, DHM is highly resource intensive and cannot fully substitute the GPU when implementing a state-of-the-art CNN. We thus propose a hybrid FPGA-GPU DL acceleration method and demonstrat

GPU and FPGA go head-to-head - Military Embedded System

The GPU + FPGA device combination performs 1.38 better than the next best device, the GPU-only, and performs 2.48 better than the worst performing device, the CPU-only implementation. Figure 8 Performance comparison in rows solved per second for when targeting CPU, GPU, FPGA, and heterogeneous combinations of devices If a kernel is data parallel, simple, and requires lots of computation, it will likely run best on the GPU. FPGAs architecture is the most compute efficient. While FPGAs and the generated custom compute pipelines can be used to accelerate almost any kernel, the spatial nature of the FPGA implementation means available FPGA resources can be a limit GPU vs FPGA for JPEG resize on-demand. Review and performance comparison with NVIDIA Tesla T4. Intel, CTAccel, Xilinx, NVIDIA, Fastvideo at high load web applications. FPGA vs GPU for image processing The proliferation of heterogeneous hardware represents a problem for programming languages such as Java that target CPUs. TornadoVM extends the Graal JIT compiler to take advantage of GPUs & FPGAs. Embedded design with FPGAs: Implementation languages; Embedded design with FPGAs: Development process; Open-source tools help simplify FPGA programming; Implementing floating-point algorithms in FPGAs or ASICs; Leveraging FPGAs for deep learning; Software tools migrate GPU code to FPGAs for AI application

Machine learning hardware (FPGAs, GPUs, CUDA) Towards

  1. FPGA-GPU Architecture for Kernel SVM Pedestrian Detection S. Bauer1, S. Köhler2, K. Doll3, U. Brunsmann2 ECVW 2010, San Francisco, 06/13/2010 1 Pattern Recognition Lab (CS 5) University Erlangen-Nuremberg, Germany 2 Laboratory for Pattern Recognition and Computational Intelligence University of Applied Sciences Aschaffenburg, German
  2. of FPGAs and GPUs on the GPU-friendly benchmark suite (Rodinia). They ported 15 of its kernels using Vivado HLS for the FPGA and OpenCL for host programs. The platforms used were a Virtex-7 FPGA and Tesla K40c GPU. Although this study includes some vision kernels such as: GICOV, Dilate, SRAD and MGVF, it was not mainly focused o
  3. Two classic lightweight encryption algorithms, Tiny Encryption Algorithm (TEA) and Extended Tiny Encryption Algorithm (XTEA), are targeted for implementation on GPUs and FPGAs. The GPU implementations of TEA and XTEA in this study depict a maximum speedup of 13x over CPU based implementation. The pipelined FPGA implementation is able to realize.
  4. fpga的定义以及和gpu的类比. 计算 fpga 遵循了相同的轨迹。我们的想法是要多多使用这一时兴的硬件,当然不是为了电路仿真,而是利用适合电路执行的计算模式,用类比的形式来看 gpu 和 fpga。 为了让 gpu 发展成今天的数据并行加速器,人们不得不重新定义 gpu.
  5. g AI and deep learning duties

While FPGA development tools for these high-level languages are capable of significant optimization of the resulting FPGA implementation of the C/C++ code algorithm, there is still something of a disconnect in that the C/C++ execution model involves the sequential execution of statements while the native FPGA environment consists of parallel hardware components GPU has high peak performance for floating-point operations. In this paper, we especially presented the baseline implementation for GPU to compare with FPGA implementation. The performance on GPU is expected to get even higher by explicitly using efficient memory system represented by local memory and texture memory on GPU FPGAs and/or GPUs holds the promise of addressing high-performance computing demands, particularly with respect to performance, power and productivity. This paper compares the sustained performance of a complex, single precision, floating-point, 1D, Fast Fourier Transform (FFT) implementation on state-of-the-art FPGA and GPU accelerators

FPGA-GPU Architecture for Kernel SVM Pedestrian Detection Sebastian Bauer1, Sebastian Kohler¨ 2, Konrad Doll3, Ulrich Brunsmann2 1Pattern Recognition Lab, Department of Computer Science University Erlangen-Nuremberg, Germany 2Laboratory for Pattern Recognition and Computational Intelligence 3Laboratory for Computer-Aided Circuit Design University of Applied Sciences Aschaffenburg, German FPGAs and GPUs have been a good intermediate point, but they will not be the best solution a year from now. New approaches are necessary. We saw FPGAs being used for machine learning, particularly in the early stages of AI when future hardware needs were very unclear, says Dennis Laudick, vice president for Arm's machine learning group FPGA estimations have been obtained using the Xilinx Power Estimator (XCE) tool and the GPU measurements using the nvidia-smi interface. Our implementation is significantly more energy efficient which is inline with what we have learnt about hardware specialization from this class In this paper, we detail the designs of three faster than state-of-the-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities ing units (CPUs) and graphics processing units (GPUs). However in recent years, research has been conducted to implement CNNs on field-programmable gate array (FPGA). The objective of this thesis is to implement a CNN on an FPGA with few hardware resources and low power consumption. The CNN we implement is for digits recognition

An FPGA can be used to solve any problem which is computable.This is trivially proven by the fact that FPGAs can be used to implement a soft microprocessor, such as the Xilinx MicroBlaze or Altera Nios II.Their advantage lies in that they are significantly faster for some applications because of their parallel nature and optimality in terms of the number of gates used for certain processes analysis comparing an existing GPU SpMV implementation to our own, novel FPGA implementation. In this analysis, we describe the challenges faced by any SpMV implementation, the unique approaches to these challenges taken by both FPGA and GPU implementations, and their relative performance for SpMV. I. INTRODUCTION FPGAs have been used as co. Unless maybe you have really nonstandard operations that you can implement directly in the FPGA. Or you have a problem that does not need any off-chip memory, so you can avoid all power for DRAMs and memory interface. DRAM is a good point: GPUs have a huge advantage here. Floating-point itself is not that hugely expensive to implement on an FPGA

(PDF) FPGA and GPU implementation of large scale SpM

  1. FPGAs or GPUs, that is the question. Since the popularity of using machine learning algorithms to extract and process the information from raw data, it has been a race between FPGA and GPU vendors to offer a HW platform that runs computationally intensive machine learning algorithms fast and efficiently
  2. The capability of FPGAs on handling different image processing components in this method is discussed. The algorithm is reformulated to build a highly efficient pipeline on FPGA. The final implementation on a Xilinx Virtex-7 FPGA is 15 times faster than the GPU implementation on two NVIDIA graphic cards (GeForce GTX 580). Not
  3. Like with GPUs, you can pack many FPGAs together and drive them from one central unit, which is exactly what people began to do. Overall, it was possible to build a big array of FPGAs more neatly and cleanly than you could with graphics cards. Using an FPGA with a careful implementation, you might get up to a GH/s, or one billion hashes per second
Simulink Real-Time FPGA I/O Modules - MATLAB & Simulink

Developing configurable GPU IP using FPGA-based prototypin

This paper presents a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBM's Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation Latest research has shown that FPGAs are a better accelerating device than GPUs or multi-core CPUs for specific issues. Therefore, this thesis deals with the implementation and assessment of ODE solvers optimized for FPGAs. Since FPGAs are relatively new in HPC, the thesis first explains the essential components of FPGAs and how to program them RNN Implementation on FPGA Xilin Yin, Libin Bai, Yue Xie, Wenxuan Mao 1 Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website of our implementation on an FPGA is only 0.18% lower than that on a graphics processing unit (GPU) in mean average precision (mAP). Under a 200 MHz working frequency, our design achieves a throughput of 111.5 giga-operations per second (GOP/s) with a 5.96 W on-chip power consumption

•GPU based implementation may allow for an all software based implementation with real-time performance for > 6 GHz NR @120KHz SCS. • No Specific FPGA HW platform and RTL design knowledge is required for modifications FPGAs vs GPUs: lessons learned (2/3) FPGA ≠ GPU, yet: same optimizations - exploit parallelism - maximize FPU utilization hide latency - optimize memory performance hide latency → prefetch maximize bandwidth → avoid bank conflicts, unit-stride access (coalescing) reuse data (caching) but achieved in very different ways

Menu introducing: the GPU 04 September 2016 on minpc. Back in 2008, I had a vision: I wanted to build a computer from scratch. I have always used microcontrollers like Atmel's AVR to power my electronic projects, and make a living of software development, and some (then) recent courses at university brought me back in contact with hardware design and FPGAs A FPGA-only implementation is infeasible due to the resource requirements for processing intermediate data arrays generated by the algorithm. We present a FPGA-GPU-CPU architecture that is a real-time implementation of the optical mapping algorithm running at 1024 fps. This represents a 273 speed up over a multi-core CPU implementation. I. In an implementation based on the Xilinx XDMA IP, for example, the host can initialise any direct memory access (DMA) transfer between the FPGA internal address space and the I/O-memory address space. This allows direct transfer between the FPGA internal address space and the mapped GPU RAM. However, the host has to initialise each data transfer High performance FPGA and GPU complex pattern Recently, GPU architectures have been used to implement tree index operations [4, 14], as well as filtering for XML Path/Twig queries [1, 20]. In this paper, we describe both FPGA- and GPU-based solutions for querying trajectory data using patterns As a result, FPGAs can hardly match up the throughput of GPUs for accelerating full-precision CNNs. Differently, for a BCNN, the operations in the convolution layers become bitwise XNORs and bit-count logic. A direct impact is that one can use LUTs instead of DSP48 slices to implement the bitwise operations on an FPGA

FPGA and GPU implementation of large scale SpMV - IEEE

FPGA may give way to ASIC in high-volume applications. AI training . . GPU parallelism well-suited for processing terabyte data sets in reasonable time. AI inference. . . . . Everyone wants in! FPGAs perhaps leading; high-end CPUs (e.g., Intel's Xeon) and GPUs (e.g., Nvidia's T4) address this market . High-speed Search. . . Synective labs | The experts on FPGA and GPU based acceleration. The leading consulting company within FPGA and ASIC design in the Nordic region. We specialize in high performance systems, creating optimized hardware and software designs where FPGAs in many cases play a key role to achieve efficient solutions. Digital Degree Fair 2020 @KTH

Nvidia updates GPU roadmap: reveals Pascal GPU

GPU DSP FPGA with HLS DSP GPU x86 FPGA with HLS X13467 Send Feedback. Introduction to FPGA Design with Vivado HLS 9 Control-centric algorithms can be implemented on both processors and FPGAs. The implementation choice depends on the reac tion time required of the algorithm gpu和cpu都是属于通用处理器,都需要进行取指令、指令译码、指令执行的过程,通过这种方式屏蔽了底层io的处理,使得软硬件解耦,但带来数据的搬移和运算无法达到更高效率,所以没有asic、fpga能耗比高。gpu和cpu之间的能耗比的差距,主要在于cpu中晶体管有大. With the right algorithm implementation, an FPGA can match the efficiency of an ASIC miner, which is often about ten times the performance-per-Watt of a GPU rig. Better yet, your FPGA miner won't turn into a paperweight if the algorithm for the cryptocurrency you are mining is updated or if you decide to start mining something else The implementation on an FPGA has an 1000s of times(!) better Performance-to-Power ratio than GPUs and CPUs. Throughout the presentation all three PDFs show other various advantages of FPGAs, such as memory-locality. The competition: GPUs. The below table is very clear with GPUs, FPGAs have much lower power consumption (e.g., 90W in Intel S10 FPGA vs. 300W in Nvidia V100 GPU). However, FPGAs also have relatively lower operating fre-quency and memory bandwidth (e.g, 14.9 GB/s in S10 FPGA vs. 900 GB/s in V100), which hinders their effective use in DNN training. Despite their complementary features, GPUs and FPGAs

An important working resource for engineers and researchers involved in the design, development, and implementation of signal processing systems The last decade has seen a rapid expansion of the use of field programmable gate arrays (FPGAs) for a wide range of applications beyond traditional digital signal processing (DSP) systems. Written by a team of experts working at the leading edge of. OpenCL implementation to support different types of devices, e.g. the AMD OpenCL implementation supports GPUs and CPUs from AMD. Due to active support for OpenCL from CPU and GPU vendors, existing worksta-tions with supported GPUs have become heterogeneous platforms for general purpose computing

FPGA vs GPU, What to Choose? - HardwareBe

fpga implementation of udp protocol using vhdl. We implemented a soft-core processor (NIOS II) from ALTERA inside the FPGA in order to reduce the cost and In this design the data are sent using the UDP protocol. This implementation allows achieving data throughput of 114 Mbytes/s 2)fpga的主频低,cpu和gpu的主频一般在1-3ghz之间,而fpga的主频一般在500mhz一下。因此,fpga的能耗要低于cpu、gpu。 3)可硬件编程. fpga可硬件编程,并且可以进行静态重复编程和动态系统重配置 Xilinx offers a comprehensive multi-node portfolio to address requirements across a wide set of applications. Whether you are designing a state-of-the art, high-performance networking application requiring the highest capacity, bandwidth, and performance, or looking for a low-cost, small footprint FPGA to take your software-defined technology to the next level, Xilinx FPGAs and 3D ICs provide. GTX 295 GPU and a Spartan-3 FPGA, while more recently, a) Pietron et al. [29] compare a human skin classi er implementation on a Tesla m2090 and a Virtex 5 device and b) the ceramic tile defect detection algo-rithm of [30] is evaluated on the 9800GT GPU and three di erent FPGAs. In a slightly di erent direction, th

FPGA implementation of HOOFR bucketing extractor-based

EMBEDDED SYSTEMS PROJECT. FPGA vs GPU Performance Comparison on the Implementation of FIR Filters Interfacing GPU FPGA G.Neeraj 1301022. CPU ,GPU ,FPGA CPUs - Ease of programming and native floating point support with complex and cumbersome memory systems, as well as significant operating.system overhead GPUs - Fine grain SIMD processing and native floating point with a streaming memory. The Best FPGA Mining Guide and Learning Platform. Here, you can find all the resources about FPGA Mining including mining rigs, bitstreams and software. For most miners, mining profitability is the most important thing we care about. We provide tools to calculate the revenue and ROI time. Hope this can help you to make the decision easier

We looked at upcoming FPGA technology advances, the rapid pace of innovation in DNN algorithms, and considered whether future high-performance FPGAs will outperform GPUs for next-generation DNNs. Our research found that FPGA performs very well in DNN research and can be applicable in research areas such as AI, big data or machine learning which requires analyzing large amounts of data Results: This paper presents and evaluates SWIFOLD: a Smith-Waterman parallel Implementation on FPGA with OpenCL for Long DNA sequences. First, we evaluate its performance and resource usage for different kernel configurations. Next, we carry out a performance comparison between our tool and other state-of-the-art implementations considering. external memory. A typical GPU implementation batches images and requires significant external memory bandwidth. In contrast, the FPGA can process one image at a time with significantly greater data reuse on chip and less external memory bandwidth. Figure 2. Neural Network Datapath FPGA IO Channels Kernel Channels/Pipes Kernel 1 Kernel 2 Kernel Will FPGA cards replace GPU cards for cryptocurrency mining? Let's review the best hardware for FPGA mining, mining profitability, and our new FPGA mining ri.. However with a PC and GPU configuration you might not achieve real-time performance, and it would definitely exclude you from Small Form Factor while requiring at least 250 watts to power the system. Of course to ensure real-time performance in a Small Form Factor low power system, you could use an FPGA

A big part of this development is due to the use of Convolutional Neural Networks (CNNs), where high performance Graphics Processing Units (GPUs) has been the most popular device. This thesis explores the use of a Field-Programmable Gate Array (FPGA), specifically an Arria 10 GX FPGA, to implement a wake up word CNN Low-level computer vision algorithms have high computational requirements. In this study, we present two real-time architectures using resource constrained FPGA and GPU devices for the computation of a new algorithm which performs tone mapping, contrast enhancement, and glare mitigation. Our goal is to implement this operator in a portable and battery-operated device, in order to obtain a low. execute the rest of the workloads. FPGAs, GPUs, and ASICs are the well-known accelerators available in the market today. In particular, FPGAs have become more widely adopted in cloud servers as well IoT platforms. Leading technology companies are pushing towards integrating FPGAs into data centers (e.g., Intel Xeon+FPGA, Microsoft Catapult)

A Look at Altera's OpenCL SDK for FPGAsReconfigurable and GPU Computing Laboratory(PDF) A real-time KLT implementation for radio-SETI

FPGA (Field Programmable Gate Array) Unlike CPU processing FPGA implementation is based on hardware. It is much faster then microcontroller. It is not good to perform operations like floating points, etc. Testing and Debugging is difficult in FPGA. Real-Time processing can be done using FPGA. ITVoyagers - itvoyagers.in 4 5 The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria Sparse matrix-vector multiplication (SpMV) is a fundamental operation for many applications. Many studies have been done to implement the SpMV on different platforms, while few work focused on the very large scale datasets with millions of dimensions. This paper addresses the challenges of implementing large scale SpMV with FPGA and GPU in the application of web link graph analysis The FPGA implementation of SNN achieves 164 frames per second (FPS) under 150 MHz clock frequency and obtains 41 times speed-up compared to CPU implementation and 22 times lower power than GPU implementation. Compared to the Minituar (Neil & Liu, 2014) and Darwin chip (Ma et al., 2017), it achieves 24.9 times and 26.2 times speed-up, respectively Finally, we deployed the improved YOLOv2 network on a Xilinx ZYNQ xc7z035 FPGA to evaluate the performance of our design. The experimental results show that the performance of our implementation on an FPGA is only 0.18% lower than that on a graphics processing unit (GPU) in mean average precision (mAP) The Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences. However, it may become impracticable in some contexts due to its high computational demands. Consequently, the computer science community has focused on the use of modern parallel architectures such as Graphics Processing Units (GPUs), Xeon Phi accelerators and Field.

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