Documentation

How Critical Path Estimation Works

HDL Coder finds the estimated critical path by performing static timing analysis with timing data from target-specific timing databases. HDL Coder has timing databases for these target devices:

To create timing databases, HDL Coder characterizes basic design components, such as Simulink^® blocks, block architectures, and subcomponents of those blocks, for specific target devices.

The code generator analyzes your model design to decompose it into the blocks and subcomponents in the timing databases. If your design consists of blocks or subcomponents in the timing databases, the code generator can estimate the timing critical path more accurately. If your design uses components that are not in the timing databases, a separate highlighting script is generated to show the uncharacterized blocks. If the timing data is incomplete for parts of your design, it is possible that the estimated critical path does not match your actual critical path.

If your target hardware is one of the target devices supported for critical path estimation, the timing numbers and estimated critical path are more accurate. If your target hardware is not a supported device, or is not in the same device family, you can estimate the critical path, but it is possible that the timing numbers are not accurate.

How to Use Critical Path Estimation

You can estimate the critical path for your design either in the Configuration Parameters dialog box or at the command line. To estimate the critical path in the Configuration Parameters dialog box:

HDL Coder generates a critical path estimation report and displays messages in the MATLAB^® Command Window that include a link to a highlighting script and a script that clears the highlighting.

To script this workflow or generate the report at the command line, enter these commands. Specify the modelname and dutname based on the design that you want to estimate the critical path for. This example uses the sfir_single model.

% Specify the model and Subsystem names
modelname = 'sfir_single';
dutname = 'sfir_single/symmetric_fir';
open_system(modelname)

% Disable HDL code generation for faster generation 
% of critical path estimation report
hdlset_param(modelname, 'CriticalPathEstimation', 'on');
hdlset_param(modelname, 'GenerateHDLCode', 'off');

% If your design contains single data types, 
% enable native floating-point support
fpconfig = hdlcoder.createFloatingPointTargetConfig('NativeFloatingPoint');
hdlset_param(modelname, 'FloatingPointTargetConfig', fpconfig);

% Generate the report
makehdl(dutname)

When you click the link to the criticalpathestimated script, the code generator highlights the critical path in the generated model. In the generated model, you see the critical path timing information and blocks that are on this path. This figure shows a section of a Simulink model that has the critical path annotated. The native floating-point operators are highlighted in light blue and the delays are highlighted in orange. The block that is part of the critical path is highlighted in dark blue with the critical path value annotated beside the block. To learn more about the different colors, see Generated Model and Validation Model.

You can clear the highlighting by clicking the link to the clearhighlighting script.

To optimize the critical path, break the critical path by adding pipeline registers. If you are using Native Floating Point, set the LatencyStrategy to Max to improve timing. Regenerate the critical path estimation report and the script that highlights the critical path in your design. You can repeat this process until your design timing meets the target frequency that you want.

Characterized Blocks

This table shows blocks that are characterized with fixed-point and single-precision native floating-point types. These blocks are part of the timing database for each supported target device.

When you use MATLAB Function blocks and generate code by using the MATLAB Datapath architecture, HDL Coder converts the MATLAB algorithm to a Simulink block diagram. In the generated model, critical path estimation can annotate the critical path inside the MATLAB Function block and across the MATLAB Function block boundary with other Simulink blocks. See also HDL Optimizations Across MATLAB Function Block Boundary Using MATLAB Datapath Architecture.

Caveats