Baseline The Hardware Functions

Before seeking to perform any hardware function optimization, it is important to understand the performance achieved with the existing code and compiler defaults, and appreciate how performance is measured. This is achieved by selecting the functions to implement hardware and building the project.

After the project has been built, a report is available in the reports section of the IDE (and provided at <project name>/<build_config>/_sds/vhls/<hw_function>/solution/syn/report/<hw_function>.rpt). This report details the performance estimates and utilization estimates.

The key factors in the performance estimates are the timing, interval, and latency in that order.

  • The timing summary shows the target and estimated clock frequency. If the estimated clock frequency is greater than the target, the hardware will not function at this clock frequency. The clock frequency should be reduced by using the Data Motion Network Clock Frequency option in the Project Settings. Alternatively, because this is only an estimate at this point in the flow, it might be possible to proceed through the remainder of the flow if the estimate only exceeds the target by 20%. Further optimizations are applied when the bitstream is generated, and it might still be possible to satisfy the timing requirements. However, this is an indication that the hardware function is not guaranteed to meet timing.
  • The initiation interval (II) is the number of clock cycles before the function can accept new inputs and is generally the most critical performance metric in any system. In an ideal hardware function, the hardware processes data at the rate of one sample per clock cycle. If the largest data set passed into the hardware is size N (e.g., my_array[N]), the most optimal II is N + 1. This means the hardware function processes N data samples in N clock cycles and can accept new data one clock cycle after all N samples are processed. It is possible to create a hardware function with an II < N, however, this requires greater resources in the PL with typically little benefit. The hardware function will often be ideal as it consumes and produces data at a rate faster than the rest of the system.
  • The loop initiation interval is the number of clock cycles before the next iteration of a loop starts to process data. This metric becomes important as you delve deeper into the analysis to locate and remove performance bottlenecks.
  • The latency is the number of clock cycles required for the function to compute all output values. This is simply the lag from when data is applied until when it is ready. For most applications this is of little concern, especially when the latency of the hardware function vastly exceeds that of the software or system functions such as DMA. It is, however, a performance metric that you should review and confirm is not an issue for your application.
  • The loop iteration latency is the number of clock cycles it takes to complete one iteration of a loop, and the loop latency is the number of cycles to execute all iterations of the loop.

The Area Estimates section of the report details how many resources are required in the PL to implement the hardware function and how many are available on the device. The key metric here is the Utilization (%). The Utilization (%) should not exceed 100% for any of the resources. A figure greater than 100% means there are not enough resources to implement the hardware function, and a larger FPGA device might be required. As with the timing, at this point in the flow, this is an estimate. If the numbers are only slightly over 100%, it might be possible for the hardware to be optimized during bitstream creation.

You should already have an understanding of the required performance of your system and what metrics are required from the hardware functions. However, even if you are unfamiliar with hardware concepts such as clock cycles, you are now aware that the highest performing hardware functions have an II = N + 1, where N is the largest data set processed by the function. With an understanding of the current design performance and a set of baseline performance metrics, you can now proceed to apply optimization directives to the hardware functions.