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Machine Learning approaches for automated selection of FPGA CAD tool parameters have been demonstrated to be useful for timing closure of FPGA designs [3], [4]. This is achieved by running the CAD tool multiple times with small variations in the the CAD parameter values. The timing slack from each run is recorded into a database along with all input parameter selections to help train a classifier...
We can use Cloud Computing and Machine Learning to help deliver timing closure of FPGA designs using InTime [2], [3]. This approach requires no modification to the input RTL and relies exclusively on manipulating the CAD tool parameters that drive the optimization heuristics. By running multiple combinations of the parameters in parallel, we learn from results and identify which parameters caused...
Machine learning and cloud computing techniques can help accelerate timing closure for FPGA designs without any modification to original RTL code. RTL is generally frozen closer to system delivery target to avoid injecting new unforeseen bugs or significantly affecting design characteristics. In these circumstances, developers trying to close timing are either at the mercy of random trials through...
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