2. numPYNQ - FPGA Rationale
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numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications. In this presentation we explain the rationale behind the use of FPGA devices in our project. numPYNQ is competing in the Xilinx Open Hardware Contest 2017.
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numPYNQ @ NGCLE@e-Novia 15.11.2017
numPYNQ is a hardware library that offers an accelerated version of NumPy core functions to be used transparently from data science applications. It implements these functions on an FPGA to provide better performance, energy efficiency, and flexibility compared to GPUs. Experimental results show speedups for tasks like matrix multiplication and cross-correlation. The library uses runtime input analysis and adaptation to optimize implementations. It has potential in the growing big data market, and the team plans partnerships and a freemium business model to commercialize numPYNQ.
1. numPYNQ - Project Presentation
numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications.
numPYNQ is competing in the Xilinx Open Hardware Contest 2017.
numPYNQ: accelerating NumPy on PYNQ
In the quest for making FPGA technology more accessible to the software community, Xilinx recently released PYNQ, a framework for Zynq that relies on Python and overlays to ease the integration of functionalities of the programmable logic into applications. In this work, we build upon this framework to enable transparent hardware acceleration for scientific computations for Zynq. We do so by providing a custom NumPy library designed for PYNQ, as it is the de-facto most used library within Data Science applications written in Python. We then demonstrate the effectiveness of the proposed approach with a use case on audio signal alignment.
5. numPYNQ - Implementation
numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications.
In this presentation we explain the implementation we did during the development of numPYNQ in the context of the Xilinx Open Hardware Contest 2017.
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3. numPYNQ - Work Organization
numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications.
In this presentation we explain the work organization for what concerns the Xilinx Open Hardware Contest 2017.
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numPYNQ @ NGCLE@e-Novia 15.11.2017
numPYNQ is a hardware library that offers an accelerated version of NumPy core functions to be used transparently from data science applications. It implements these functions on an FPGA to provide better performance, energy efficiency, and flexibility compared to GPUs. Experimental results show speedups for tasks like matrix multiplication and cross-correlation. The library uses runtime input analysis and adaptation to optimize implementations. It has potential in the growing big data market, and the team plans partnerships and a freemium business model to commercialize numPYNQ.
1. numPYNQ - Project Presentation
numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications.
numPYNQ is competing in the Xilinx Open Hardware Contest 2017.
numPYNQ: accelerating NumPy on PYNQ
In the quest for making FPGA technology more accessible to the software community, Xilinx recently released PYNQ, a framework for Zynq that relies on Python and overlays to ease the integration of functionalities of the programmable logic into applications. In this work, we build upon this framework to enable transparent hardware acceleration for scientific computations for Zynq. We do so by providing a custom NumPy library designed for PYNQ, as it is the de-facto most used library within Data Science applications written in Python. We then demonstrate the effectiveness of the proposed approach with a use case on audio signal alignment.
5. numPYNQ - Implementation
numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications.
In this presentation we explain the implementation we did during the development of numPYNQ in the context of the Xilinx Open Hardware Contest 2017.
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The document describes numPYNQ, a hardware library that offers an accelerated version of NumPy for use on FPGAs. It aims to provide high performance and low energy consumption for tasks like signal processing, machine learning, and image analysis. The library addresses challenges of FPGA development like performance, energy usage, and design time. It uses an overlay and Python API to make FPGA programming accessible for data scientists. The presentation outlines numPYNQ's technical approach, potential clients in big data companies, go-to-market strategy, revenue model, competition, and roadmap.
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numPYNQ aims to create an hardware library that features accelerated versions of the core functions of numPy, a software library for scientific computing commonly used in Python applications.
In this presentation we explain the work organization for what concerns the Xilinx Open Hardware Contest 2017.
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Personalized Pagerank (PPR) is a common building block of Recommender Systems. In this setting, the computation of the topmost ranked vertices needs to be executed extremely fast, with low latency and possibly for multiple elements concurrently.
In this work, we present a high throughput implementation of the PPR algorithm leveraging a reduced precision-fixed point computation in order to achieve up to 6x speedup and 42x lower energy consumption with respect to a state of the art CPU implementation.
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★ Resources ★
Event Page: http://connect.linaro.org/resource/hkg18/hkg18-301/
Presentation: http://connect.linaro.org.s3.amazonaws.com/hkg18/presentations/hkg18-301.pdf
Video: http://connect.linaro.org.s3.amazonaws.com/hkg18/videos/hkg18-301.mp4
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★ Event Details ★
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【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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If you like what you read be sure you ♥ it below. Thank you!
Python* Scalability in Production Environments
This document discusses scaling Python performance in production environments. It introduces the Intel Distribution for Python, which provides optimized versions of NumPy, SciPy, and Scikit-Learn using Intel MKL to accelerate linear algebra and machine learning algorithms. It also supports parallelism through MPI, TBB for multithreading, and integration with big data frameworks. Profiling tools like Intel VTune Amplifier help optimize mixed-language Python applications for Intel architectures. The goal is to make Python usable for high performance computing and big data workloads while maintaining its ease of use.
“How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibili...
“How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibili...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/how-do-we-enable-edge-ml-everywhere-data-reliability-and-silicon-flexibility-a-presentation-from-edge-impulse/
Zach Shelby, Co-founder and CEO of Edge Impulse, presents the “How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibility” tutorial at the May 2022 Embedded Vision Summit.
In this talk, Shelby reveals insights from the company’s recent global edge ML developer survey, which identified key barriers to machine learning adoption, and shares the company’s vision for how the industry can overcome these obstacles. Unsurprisingly, the first critical obstacle identified by the survey is data. But the issue isn’t simply a lack of massive datasets, as is often assumed. On the contrary, the biggest opportunities in ML will be enabled by highly custom, industry-specific and even user-specific data. We need to master data lifecycle and active learning techniques that enable developers to move quickly from “zero to dataset.”
The real and perceived inability of today’s ML algorithms to reach the ultra-high accuracy needed in industrial systems is another key barrier. New techniques for explainable ML, better testing, sensor fusion and model fusion will increasingly allow developers to achieve industrial-grade reliability. Finally, in order to accelerate ML adoption in embedded products, we must recognize that most developers can’t immediately upgrade their systems to use the latest chips — a problem that is compounded by today’s chip shortages. To enable ML everywhere, we have to find ways to deploy ML on today’s silicon, while ensuring a smooth transition to new devices with AI acceleration in the future.Improve productivity with the Lenovo ThinkCentre M90a Pro Gen 4 all-in-one de...
Improve productivity with the Lenovo ThinkCentre M90a Pro Gen 4 all-in-one de...Principled Technologies
Compared to the Apple iMac, the ThinkCentre M90a Pro Gen 4 offered stronger performance in benchmarks and hand-timed tasks
Conclusion
Before you invest in an all-in-one PC for yourself or your workforce, make sure it’s up to the task. Our tests showed that the Lenovo ThinkCentre M90a Pro Gen 4 was a good multitasking system, consistently achieving higher scores on benchmark tests and completing hand-timed workflows faster than the iMac. Plus, the ThinkCentre M90a Pro Gen 4 offered strong speaker output and microphone quality for a positive videoconferencing experience and it arrives at the door in thoughtful, sustainable packing that lessens your environmental impact.Capi snap overview
copyright
Alexandre Castellane, alexandre.castellane@fr.ibm.com
Bruno Mesnet, bruno.mesnet@fr.ibm.com
PowerTM Systems CAPI Hardware acceleration
Montpellier Cognitive Systems lab
Advanced data science algorithms applied to scalable stream processing by Dav...
https://www.bigdataspain.org/2016/program/fri-advanced-data-science-algorithms-applied-scalable-stream-processing.html
https://www.youtube.com/watch?v=raul7zMBmfM&index=28&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&t=38s
Netezza Architecture and Administration
The document discusses the Netezza TwinFin 12 appliance hardware components and administration. It describes the key hardware components including snippet blades (SPUs), host servers, and storage arrays. It provides details on monitoring the status of hardware components like the hosts, SPUs, data slices, and disks. It also covers topics like hardware roles, states, storage design, high availability configuration, and system administration functions.
Webinar: Using Monza R6 to Enable Item Intelligence
Monza R6 is Impinj’s latest high-performance RAIN RFID tag chip which enables businesses to gain Item Intelligence by connecting items to the Internet. Monza R6 allows retailers and brand owners to achieve significant system performance improvements, unmatched data integrity and the lowest applied tag cost in their RFID operations while using smaller and more versatile tags that extend the benefits of Item Intelligence to new product categories. Innovations in Monza R6 such as Integra, AutoTune and Enduro deliver rapid and consistent scaling of use cases such as retail omnichannel fulfillment and personalized in-store consumer experiences.
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
Session ID: HKG18-301
Session Name: HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integrated Processors
Speaker: Glenn Steiner
Track: LITE
★ Session Summary ★
Key Takeaways:
With the drive to increase integration, reduce system costs, accelerate performance, and enhance reliability, software developers are discovering the processor they would like to target is simply not fast enough. This session will help you the system architect or software developer understand how you can architect and develop software on an FPGA integrated processor, and accelerate software code via FPGA accelerators.
Abstract:
As a software developer, in order to meet system level performance requirements, you may have realized that your next software project will be targeting a processor inside of an FPGA. How will this impact your development process and what benefits might you gain with this tight integration of processor and FPGA? Starting from the basics of what FPGAs are (in terms of software programming), this session will provide a simple to understand primer of what modern FPGAs with embedded processors can do. We will wrap up with examples of how high level synthesis tools can move software to programmable logic hardware enabling dramatic software acceleration.
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★ Resources ★
Event Page: http://connect.linaro.org/resource/hkg18/hkg18-301/
Presentation: http://connect.linaro.org.s3.amazonaws.com/hkg18/presentations/hkg18-301.pdf
Video: http://connect.linaro.org.s3.amazonaws.com/hkg18/videos/hkg18-301.mp4
---------------------------------------------------
★ Event Details ★
Linaro Connect Hong Kong 2018 (HKG18)
19-23 March 2018
Regal Airport Hotel Hong Kong
---------------------------------------------------
Keyword: LITE
'http://www.linaro.org'
'http://connect.linaro.org'
---------------------------------------------------
Follow us on Social Media
https://www.facebook.com/LinaroOrg
https://www.youtube.com/user/linaroorg?sub_confirmation=1
https://www.linkedin.com/company/1026961
A Methodology for Automatic GPU Kernel Optimization
In recent years Graphic Processing Units (GPUs) have seen widespread adoption in many scientific fields, from Machine Learning (ML) to Genomics. Their use makes it possible to achieve significant speedups and improvements in power efficiency over computationally intensive algorithms compared to General Purpose Central Processing Units (CPUs).
However, algorithms require specific knowledge of the GPU architecture and expertise to achieve significant results.
In this work, we describe a methodology for automatic GPU kernel optimization.
Our methodology exploits the Berkeley Roofline Model to perform a performance analysis of the algorithm considered and aims to increase the accessibility of GPU programming automatizing the optimization process of the kernel.
We provide an in-depth analysis of this methodology, an overview on the state of the art, and a description of a tool we developed that automatically applies our methodology to obtain a highly optimized GPU version of two of the most popular algorithms used in computational biology, the X-drop and Smith-Waterman algorithms.
The Smith-Waterman algorithm is one of the most used algorithms in genomics pipelines.
The algorithm finds the optimal local alignment between two genomic sequences, at the cost of being particularly compute-intensive.
The popular X-drop algorithm reduces the time required by the alignment by searching only for high-quality alignments.
The algorithms accelerated using our methodology achieve more than 6x and 3x speed-up, for the X-drop and Smith-Waterman algorithms respectively, with respect to the state of the art implementation of these algorithms.
Micron: Seamless Prediction at the Edge Using TensorFlow on FPGAs
IoT is exploding, with 50 billion sensors predicted by 2020. These sensors create daunting data streams. To businesses, though, the real value comes from learning what patterns lye hidden in these streams. At the edge, power constraints suggest using technologies like FPGAs. However, they have been notoriously difficult to program—until now. Come hear how Micron can deploy deep-learning models created with Tensorflow—with no code change—on power-efficient Micron FPGA systems.
Netezza TwinFin12 Architecture Administration
The nzhw show command displays the hardware components of the Netezza TwinFin 12 appliance. It shows the key components like the SPUs, disks, disk enclosures, fans, power supplies, and management modules. It provides details on the component ID, location, role, and state. Monitoring these hardware components is important for the health and performance of the Netezza system.
Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...
In part two of this presentation, continue to learn about the latest developments and tools for high-performance Python* for scikit-learn*, NumPy, SciPy, Pandas, mpi4py, and Numba*. Apply low-overhead profiling tools to analyze mixed C, C++, and Python applications to detect performance bottlenecks in the code and to pinpoint hotspots as the target for performance tuning. Get the best performance from your Python application with the best-known methods, tools, and libraries.
Intel® Curie™ Module High Density System-in-Package for IoT 2016 teardown rev...
Ultra-small, low-power hardware module for wearables
Intel®, whose microprocessors can be found in almost every computer worldwide, has commenced the 2016 “wearables race” by introducing the Intel® Curie™ module. This tiny system-in-package (SiP) can be found in the latest Arduino® Board: the Genuino 101 for the European version and the Arduino 101 for the US version. The module includes the Intel Quark chip, Bluetooth low-energy radio, sensors, and battery charging, all in a single package of less than 150mm3.
This complete, low-power solution comes with computing, motion sensing, Bluetooth low energy, battery charging, and pattern matching capabilities for optimal sensor data analysis, enabling quick and easy identification of actions and motions. The module is packaged in a tiny form factor and runs a new software platform created especially for the Intel Curie module.
Located on the main board, the system uses non-conventional packaging developed by Intel. The main microcontroller is located at the bottom of the package, with features like Bluetooth and sensors placed on top.
More information on that report at http://www.i-micronews.com/reports.html
Netezza pure data
The document discusses IBM PureData System for Analytics, which is powered by Netezza technology. It provides an overview of PureData and Netezza, the PureData hardware architecture using S-Blades and FPGA technology for parallel processing, and analytics capabilities with PureData like in-database tools.
NiPS Summer School 2019 Lecture
The document discusses the BondMachine toolkit for enabling machine learning on FPGA. It provides technological background on challenges in computing like the von Neumann bottleneck and need for energy efficiency. FPGA technology and its use in computing is described, addressing challenges like legacy code porting. Current computer architectures are multi-core and heterogeneous. The BondMachine project aims to build an architecture adapting to computational problems using FPGA for concurrency and specialization. Abstractions and interfaces between layers are important for system quality.
Enabling Transparent Hardware Acceleration on Zynq SoC for Python Data Scienc...
Enabling Transparent Hardware Acceleration on Zynq SoC for Python Data Scienc...NECST Lab @ Politecnico di Milano
In the quest for making FPGA technology more accessible to the software community, Xilinx recently released PYNQ, a framework for Zynq that relies on Python and overlays to ease the integration of functionalities of the programmable logic into applications. In this work, we build upon this framework to enable transparent hardware acceleration for scientific computations for Zynq. We do so by providing a custom NumPy library designed for PYNQ, as it is the de-facto most used library within Data Science applications written in Python. We then demonstrate the effectiveness of the proposed approach with a use case on audio signal alignment.PGI Compilers & Tools Update- March 2018
This document provides an update on PGI compilers and tools for heterogeneous supercomputing. It discusses PGI's support for OpenACC directives to accelerate applications on multicore CPUs and NVIDIA GPUs from a single source. It highlights new compiler features including support for Intel Skylake, AMD EPYC and IBM POWER9 CPUs as well as NVIDIA Volta GPUs. Benchmark results show strong performance of OpenACC applications on these platforms. The document also discusses the growing adoption of OpenACC in HPC applications and resources available to support OpenACC development.
Similar to 2. numPYNQ - FPGA Rationale (20)
Intel Distribution for Python - Scaling for HPC and Big Data
Intel Distribution for Python - Scaling for HPC and Big Data
“How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibili...
“How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibili...
Improve productivity with the Lenovo ThinkCentre M90a Pro Gen 4 all-in-one de...
Improve productivity with the Lenovo ThinkCentre M90a Pro Gen 4 all-in-one de...
Advanced data science algorithms applied to scalable stream processing by Dav...
Advanced data science algorithms applied to scalable stream processing by Dav...
Webinar: Using Monza R6 to Enable Item Intelligence
Webinar: Using Monza R6 to Enable Item Intelligence
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
HKG18-301 - Dramatically Accelerate 96Board Software via an FPGA with Integra...
A Methodology for Automatic GPU Kernel Optimization
A Methodology for Automatic GPU Kernel Optimization
Micron: Seamless Prediction at the Edge Using TensorFlow on FPGAs
Micron: Seamless Prediction at the Edge Using TensorFlow on FPGAs
Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...
Accelerate Your Python* Code through Profiling, Tuning, and Compilation Part ...
Intel® Curie™ Module High Density System-in-Package for IoT 2016 teardown rev...
Intel® Curie™ Module High Density System-in-Package for IoT 2016 teardown rev...
Enabling Transparent Hardware Acceleration on Zynq SoC for Python Data Scienc...
Enabling Transparent Hardware Acceleration on Zynq SoC for Python Data Scienc...
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一比一原版西三一大学毕业证(TWU毕业证书)学历如何办理
加急办理【微信号:176555708】【加急办理(TWU毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
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2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
买(usyd毕业证书)澳洲悉尼大学毕业证研究生文凭证书原版一模一样
原版定制【微信:bwp0011】《(usyd毕业证书)澳洲悉尼大学毕业证研究生文凭证书》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
按照学校原版(UPenn文凭证书)宾夕法尼亚大学毕业证快速办理
办理【(UPenn毕业证书)宾夕法尼亚大学毕业证】【176555708微信号】存档可查的成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版不列颠哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
快速办理【微信号:176555708】【快速办理(UBC毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号:176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(SBU毕业证书)肯特州立大学毕业证如何办理
原版办理【微信号:BYZS866】【(SBU毕业证书)肯特州立大学毕业证】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
欧洲杯体彩-欧洲杯体彩比赛投注-欧洲杯体彩比赛投注官网|【网址🎉ac99.net🎉】
【网址🎉ac99.net🎉】欧洲杯体彩是独立的博彩公司。该公司最初专门从事在线体育博彩，现在合并了在线娱乐场。该公司最初成立时以其前董事长Victor Chandler的名字命名，之后更名为欧洲杯体彩。欧洲杯体彩现由商人和赛马主，迈克尔·塔博尔拥有，运营总部设在直布罗陀。平台拥有体育博彩（沙巴）、真人娱乐场、电子游戏、金融投注等游戏项目，支持手机投注，优惠活动很丰富。
一比一原版圣托马斯大学毕业证(UST毕业证书)学历如何办理
精仿办理【微信号:176555708】【精仿办理(UST毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号:176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
欧洲杯投注-欧洲杯投注押注app-欧洲杯投注押注app官网|【网址🎉ac10.net🎉】
【网址🎉ac10.net🎉】欧洲杯投注在多数英超比赛球场都设有投注点，其业务现已扩展到西班牙。欧洲杯投注支持19种语言和货币。更为重要的是，欧洲杯投注使用市场上最为先进的安全系统，为用户提供全年365天，每天24小时的专业服务。
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【网址🎉ac10.net🎉】欧洲杯赌钱是世界上最大的网上博彩公司之一，于直布罗陀注册，在200个国家拥有超过3500万客户。在国际上都可以称得上是极其优秀的博彩公司。总部位于英国Stoke-on-Trent，欧洲杯赌钱在世界各地的雇员超过600名。该公司登录亚洲市场多年，近年针对亚洲的市场拓展的很快。它的特点是投注赔率富于变化，同时对于冷门赛事，赔率变化幅度会大于其它博彩公司。欧洲杯赌钱在终赔阶段往往向立博、韦德等靠拢，一旦差异很大，往往会出现问题。
"IOS 18 CONTROL CENTRE REVAMP STREAMLINED IPHONE SHUTDOWN MADE EASIER"
In iOS 18, Apple has introduced a significant revamp to the Control Centre, making it more intuitive and user-friendly. One of the standout features is a quicker and more accessible way to shut down your iPhone. This enhancement aims to streamline the user experience, allowing for faster access to essential functions. Discover how iOS 18's redesigned Control Centre can simplify your daily interactions with your iPhone, bringing convenience right at your fingertips.
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2. numPYNQ - FPGA Rationale
- 1. numPYNQ Riccardo Pressiani riccardo.pressiani@mail.polimi.it Luca Stornaiuolo luca.stornaiuolo@mail.polimi.it Filippo Carloni filippo.carloni@mail.polimi.it FPGA Rationale
- 2. numPYNQ 2 Huge amount of data that need to be processed to get aggregated information
- 3. numPYNQ 3 Huge amount of data that need to be processed to get aggregated information
- 4. numPYNQ 4 Huge amount of data that need to be processed to get aggregated information Processing System
- 6. numPYNQ Comparison 6 Easiness of use Performance Flexibility GPU ASIC CPU FPGA
- 7. numPYNQ Comparison 7 Easiness of use Performance Flexibility GPU ASIC CPU FPGA
- 8. numPYNQ Comparison 8 Easiness of use Performance Flexibility GPU ASIC CPU
- 9. numPYNQ 9 Luca Stornaiuolo luca.stornaiuolo@mail.polimi.it Riccardo Pressiani riccardo.pressiani@mail.polimi.it Filippo Carloni filippo.carloni@mail.polimi.it Thanks for the attention!
- 10. numPYNQ an efficient implementation of the NumPy library for PYNQ https://twitter.com/numPYNQ_NECST https://www.facebook.com/numPYNQ https://www.slideshare.net/numPYNQ_at_NECST