Politecnico di Milano
Developing FPGA-accelerated cloud applications with SDAccel: Theory
Politecnico di Milano

Developing FPGA-accelerated cloud applications with SDAccel: Theory

Taught in English

Some content may not be translated

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Gain insight into a topic and learn the fundamentals


(76 reviews)

Intermediate level

Recommended experience

19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • The theory on how to develop FPGA-accelerated applications with SDAccel.

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There are 6 modules in this course

From the mid-1980s, reconfigurable computing has become a popular field due to the FPGA technology progress. An FPGA is a semiconductor device containing programmable logic components and programmable interconnects but no instruction fetch at run time, that is, FPGAs do not have a program counter. In most FPGAs, the logic components can be programmed to duplicate the functionality of basic logic gates or functional Intellectual Properties (IPs). FPGAs also include memory elements composed of simple flip-flops or more complex blocks of memories. Hence, FPGA has made possible the dynamic execution and configuration of both hardware and software on a single chip. This module provides a detailed description of FPGA technologies starting from a general description down to the discussion on the low-level configuration details of these devices, to the bitstream composition and the description of the configuration registers.

What's included

9 videos2 quizzes

The Xilinx SDAccel Development Environment let the user express kernels in OpenCL C, C++ and RTL (as an example we can think of, SystemVerilog, Verilog or VHDL) to run on Xilinx programmable platforms. The programmable platform is composed of (1) the SDAccel Xilinx Open Code Compiler (XOCC), (2) a Device Support Archive (DSA) which describes the hardware platform, (3) a software platform, (4) an accelerator board, and5. last but not least, the SDAccel OpenCL runtime. Within this module, after an introduction to OpenCL, we are going to see how this language has been sued in SDAccel and the main "components" of this toolchain.

What's included

7 videos1 reading1 quiz

Within this module, Before getting into the optimisation, we will first understand how an FPGA is working, also from a computational point of view. Although the traditional FPGA design flow is more similar to a regular IC than a processor, an FPGA provides significant cost advantages in comparison to an IC development effort and offers the same level of performance in most cases. Another advantage of the FPGA when compared to the IC is its ability to be dynamically reconfigured. This process, which is the same as loading a program in a processor, can affect part or all of the resources available in the FPGA fabric. When compared with processor architectures, the structures that comprise the FPGA fabric enable a high degree of parallelism in application execution. The custom processing architecture generated by SDAccel for an OpenCL kernel presents a different execution paradigm. This must be taken into account when deciding to port an application from a processor to an FPGA. To better understand such a scenario we will briefly compare a processor sequential execution with the intrinsic parallel nature of an FPGA implementation. Furthermore, within this module we are going to familiarise ourselves with the application optimisation flow.The Xilinx SDAccel Environment is a complete Software Development Environment, for creating, compiling, and optimising OpenCL applications with the objective of being accelerated on Xilinx FPGAs. From a designer perspective we can organise the flow for optimising an application in the SDAccel Environment as a three phases flow. Those three phases are: (1) baselining functionalities and performance, (2) optimising data movement and (3) optimising kernel computation

What's included

5 videos1 reading1 quiz

In this module we will provide a bird's eye view on the available SDAccel optimisations. The presented optimisations are not the only available ones, but they are more a list of recommendations to optimise the performance of an OpenCL application that have to be used as a starting point for ideas to consider or investigate further. Within this context we will organise these “recommendations” in three sets of optimisations: (1) arithmetic optimisations, (2) data-related optimisations, and finally (3) memory-related optimisations.

What's included

6 videos2 readings1 quiz

After an overall description of possibile optimisations, within this module we will focus on four specific optimisations (1) loop unrolling, (2) loop pipelining, (3) array partitioning and (4) the host optimisations. First, we will describe loop unrolling which means to unroll the loop iterations so that, the number of iterations of the loop reduces, and the loop body performs extra computation. This technique allows to expose additional instruction level parallelism that Vivado HLS can exploit to implement the final hardware design. After that we will present the loop pipelining optimisation, where we will move from a sequential execution of the loop iterations to a pipelined execution in which the loop iterations are overlapped in time. After that we will present the array partitioning optimisation which allows to optimise the usage of BRAM resources in order to improve the performance of the kernel. Finally, at the end of this module we are going to discuss optimisations related to the host system that is responsible for transferring the data to and from the FPGA board, as well as to send the command to start the execution of a kernel.

What's included

6 videos2 readings1 quiz

What's included

3 videos1 reading1 quiz


Instructor ratings
4.5 (15 ratings)
Marco Domenico Santambrogio
Politecnico di Milano
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