Blog : machine learning

In FPGA design, the success or failure of compilation plays a crucial role in determining the efficiency and effectiveness of the overall design process. Traditionally, designers had to invest significant time and resources into running compilations, often spanning several days, only to find out that they failed due to various reasons. However, with the advent […]

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Placement and routing failures during the FPGA backend flow are frequent with many FPGA designs especially when these involve multiple physical constraints. This new whitepaper introduces the Machine-Learning(ML)-based approach implemented in the InTime toolset to transform these failures to a less complex problem of timing convergence. InTime identifies the root causes of the placement or […]

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In FPGA design optimization, it is practically impossible to predict the effect of every possible placement and route in advance. Exploring the endless combinations of settings to find good ones is like navigating correctly through a teeming traffic thoroughfare. So drawing inspiration from autonomous driving that successfully solved the road navigation problems, how do you […]

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It is the start of a brand new day. You come to the office, open up a medical imaging design that met timing the day before and finds that a bugfix made by someone else has led to timing failures. In the demanding world of high-performance FPGA design, there is no free lunch. We take pains […]

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