This course is for anyone passionate in learning how to develop FPGA-accelerated applications with SDAccel! We are entering in an era in which technology progress induces paradigm shifts in computing! As a tradeoff between the two extreme characteristics of GPP and ASIC, we can find a new concept, a new idea of computing... the reconfigurable computing, which has combined the advantages of both the previous worlds. Within this context, we can say that reconfigurable computing will widely, pervasively, and gradually impact human lives. Hence, it is time that we focus on how reconfigurable computing and reconfigurable system design techniques are to be utilised for building applications. One one hand reconfigurable computing can have better performance with respect to a software implementation but paying this in terms of time to implement. On the other hand a reconfigurable device can be used to design a system without requiring the same design time and complexity compared to a full custom solution but being beaten in terms of performance. Within this context, the Xilinx SDx tools, including the SDAccel environment, the SDSoC environment, and Vivado HLS, provide an out-of-the-box experience for system programmers looking to partition elements of a software application to run in an FPGA-based hardware element, and having that hardware work seamlessly with the rest of the application running in a processor or embedded processor. The out-of-the-box experience will provide interesting and, let us say, “good enough” results for many applications. However, this may not be true for you, you may be looking for better performance, data throughput, reduced latency, or to reduce the resources usage... This course is focusing exactly on this. After introducing you to the FPGAs we are going to dig more into the details on how to use Xilinx SDAccel providing you also with working examples on how to optimize the hardware logic to obtain the best of of your hardware implementations. In this case, certain attributes, directives, or pragmas, can be used to direct the compilation and synthesis of the hardware kernel, or to optimise the function of the data mover operating between the processor and the hardware logic. Furthermore, In this course we are going to focus on distributed, heterogeneous infrastructures, presenting how to bring your solutions to life by using the Amazon EC2 F1 instances.
Kernel optimization: loop unrolling 1/2
您將學習的技能
Programming Principles, Computer Architecture, Programming Language Concepts
審閱
It is a good course to know the basic of Xilinx sdaccel with a bit more inclination towards the history of the development of FPGA.
A very nice introduction course to give you a detailed look at how FPGA can be used to accelerate software applications.
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.
教學方
Marco Domenico Santambrogio
Associate Professor
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