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Developing FPGA-accelerated cloud applications with SDAccel: Practice

Approx. 13 hours to complete

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Course Summary

This course on FPGA and SDAccel Practice will teach you how to design, develop and deploy FPGA-accelerated software applications using SDAccel development environment. You will learn the basics of FPGA, how to optimize performance of your application, and how to debug and profile your application to achieve maximum performance.

Key Learning Points

Learn to design and develop FPGA-accelerated software applications using SDAccel development environment
Optimize the performance of your application and debug and profile it to achieve maximum performance
Understand the basics of FPGA and how to use it in your software applications

Job Positions & Salaries of people who have taken this course might have

FPGA Engineer
- USA: $95,000
- India: ₹1,200,000
- Spain: €45,000
Embedded Systems Engineer
- USA: $85,000
- India: ₹900,000
- Spain: €35,000
Software Development Engineer
- USA: $90,000
- India: ₹1,000,000
- Spain: €40,000

Learning Outcomes

Design and develop FPGA-accelerated software applications using SDAccel development environment
Optimize the performance of your application and debug and profile it to achieve maximum performance
Understand the basics of FPGA and how to use it in your software applications

Prerequisites or good to have knowledge before taking this course

Basic knowledge of C or C++ programming
Familiarity with software development and debugging

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Hands-on practice exercises

Similar Courses

FPGA Design for Embedded Systems
Introduction to FPGA Design for Embedded Systems

Related Education Paths

Notable People in This Field

Steve Leibson
Clive Maxfield

Related Books

Description

This course is for anyone passionate about learning how to develop FPGA-accelerated applications with SDAccel!

The more general purpose you are, the more flexible you are and the more kinds of programs and algorithms you can execute on your underlying computing infrastructure. All of this is terrific, but there is no free food and this is happening, quite often, by losing in efficiency. This course will present several scenarios where the workloads require more performance than can be obtained even by using the fastest CPUs. This scenario is turning cloud and data center architectures toward accelerated computing. Within this course, we are going to show you how to gain benefits by using Xilinx SDAccel to program Amazon EC2 F1 instances. We are going to do this through a working example of an algorithm used in computational biology. The huge amount of data the algorithms need to process and their complexity raised the problem of increasing the amount of computational power needed to perform the computation. In this scenario, hardware accelerators revealed to be effective in achieving a speed-up in the computation while, at the same time, saving power consumption. Among the algorithms used in computational biology, the Smith-Waterman algorithm is a dynamic programming algorithm, guaranteed to find the optimal local alignment between two strings that could be nucleotides or proteins. In the following classes, we present an analysis and successive FPGA-based hardware acceleration of the Smith-Waterman algorithm used to perform pairwise alignment of DNA sequences. Within this context, this course is focusing on distributed, heterogeneous cloud infrastructures, providing you details on how to use Xilinx SDAccel, through working examples, to bring your solutions to life by using the Amazon EC2 F1 instances.

Outline

Reconfigurable cloud infrastructure
Course introduction
An overview of cloud infrastructure
Cloud Computing: few definitions
Reconfigurable acceleration in the Cloud
Reconfigurable acceleration in the Cloud: Intel FPGA-based solutions
Reconfigurable acceleration in the Cloud: Xilinx FPGA-based solutions
Reconfigurable acceleration in the Cloud: from the past, to the future
An introduction to the AWS EC2 F1 instances
QUIZ 1
QUIZ 2
QUIZ 3

On how to accelerate the cloud with SDAccel
Applicative domains and Victor's story
F1: instances and FPGA description
How FPGA Acceleration Works on AWS
AWS F1 Platform Model
Creating Kernels from RTL IP, C/C++, OpenCL
Compiling the Platform
Creating an Amazon FPGA Image
Developing and Executing a Host Application on F1
Start Accelerating
QUIZ 4
QUIZ 5
QUIZ 6

Summing things up: the Smith-Waterman algorithm
Problem description
Algorithm and code analysis
Roofline model 1/2
Roofline model 2/2
Code profiling
Static Code Analysis 1/2
Static Code Analysis 2/2
Performance Prediction via Roofline Model
SDAccel Environment Profiling and Optimisation Guide
QUIZ 7

The Smith-Waterman example in details
A first implementation 1/3
A first implementation 2/3
A first implementation 3/3
Parallelism in the Smith-Waterman Algorithm
Systolic Array Architecture 1/2
Systolic Array Architecture 2/2
Input Compression
Shift Register
Dual Physical Ports
Smith-Waterman accelerated on the Amazon EC2 F1 instances 1/3
Smith-Waterman accelerated on the Amazon EC2 F1 instances 2/3
Smith-Waterman accelerated on the Amazon EC2 F1 instances 3/3
Sources Codes
Source Codes
QUIZ 8
QUIZ 9

Course conclusions
Closing remarks and future directions
Architectural optimizations for high performance and energy efficient Smith-Waterman implementation on FPGAs using OpenCL

Summary of User Reviews

Learn FPGA SDAccel Practice with Coursera. Students enjoyed this course and found it to be informative and engaging. One key aspect that many users appreciated was the practical and hands-on approach to learning.

Pros from User Reviews

Practical and hands-on approach to learning
Great for beginners
Good balance between theory and practice
Instructors are knowledgeable and helpful

Cons from User Reviews

Some assignments were difficult
Not enough explanation on some topics
Lack of interaction with other students
No real-world project examples

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