Developing FPGA-accelerated cloud applications with SDAccel: Theory

Go to Course: https://www.coursera.org/learn/fpga-sdaccel-theory

Introduction

### Course Review: Developing FPGA-Accelerated Cloud Applications with SDAccel: Theory As we navigate through a rapidly evolving technological landscape, the course **"Developing FPGA-Accelerated Cloud Applications with SDAccel: Theory"** stands out as an essential learning experience for anyone interested in reconfigurable computing and FPGA (Field Programmable Gate Array) technologies. This course, available on Coursera, is meticulously designed for those who are eager to delve into the world of FPGA-accelerated applications, utilizing the Xilinx SDAccel Development Environment. #### Overview of the Course The course sets the stage for understanding the transformational journey of computing from traditional General Purpose Processors (GPP) and Application-Specific Integrated Circuits (ASIC) to a hybrid model that leverages the best of both worlds through reconfigurable computing. Notably, reconfigurable computing offers dynamic adaptability, which is ideal for modern computational demands in cloud environments. The course takes a structured approach, guiding students through a comprehensive syllabus that begins with fundamental FPGA technologies and gradually progresses toward the intricacies of application optimization. It effectively introduces learners to a world where hardware can be programmed to execute specific tasks, allowing for unparalleled performance and efficiency. #### Course Syllabus Breakdown 1. **Familiarize Yourself with FPGA Technologies** - This module serves as an introduction to FPGA technology, detailing its historical context, components, and functionality. It provides insights into how FPGAs can mimic traditional logic gates and perform diverse computational tasks on a single chip. 2. **A Bird’s Eye View on SDAccel** - Here, students are introduced to the Xilinx SDAccel Development Environment, learning how to express kernels using OpenCL C, C++, and RTL. This foundational understanding is critical for working with programmable platforms and begins to illustrate how SDAccel streamlines the development of cloud-accelerated applications. 3. **On How to Optimize Your System** - Learners explore optimization techniques for FPGA systems, grasping how to leverage the unique architecture of FPGAs for improved performance. This module emphasizes understanding the FPGA's operational dynamics and covers optimization flows vital for effective application enhancement. 4. **Optimize Your System via SDAccel** - This segment presents a deeper dive into optimization strategies specific to SDAccel. By focusing on arithmetic, data-related, and memory-related optimizations, participants gain practical knowledge that can be applied directly to performance-heavy computing tasks. 5. **Other Optimizations** - In this module, advanced optimization techniques are explored, such as loop unrolling and array partitioning. These concepts are not only relevant but are also critical to maximizing resource utilization and improving execution efficiency. 6. **Introduction to FPGA-Augmented Cloud Infrastructures** - To round off the course, this section introduces the concept of integrating FPGA technology within cloud infrastructures, establishing the connection between these advanced capabilities and real-world applications. #### Recommendation I highly recommend this course for professionals, students, and enthusiasts who are keen to expand their knowledge and skills in the fields of reconfigurable computing and FPGA development. The curriculum is structured logically, catering to both newcomers and those with a foundational understanding of technology. The course material is engaging, and the practical insights gain significant relevance as businesses increasingly adopt FPGA solutions for cloud applications. Moreover, the hands-on nature of the course allows learners to not just grasp theoretical concepts but also apply them in practical scenarios. Whether you're looking to enhance your skill set for career advancement or simply wish to explore a pioneering area of technology, this course is an excellent investment in your professional development. **Final Thoughts** In a world where technology continually reshapes our understanding of computing, being well-versed in FPGA technology and cloud computing is invaluable. The "Developing FPGA-Accelerated Cloud Applications with SDAccel: Theory" course on Coursera empowers participants to harness this knowledge effectively, preparing them for the challenges and opportunities that lie ahead in the dynamic field of computing. Enroll today and take the first step towards mastering FPGA-accelerated applications!

Syllabus

Familizarize youself with FPGA technologies

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.

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.

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

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.

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.