InTime is a machine learning software that solves FPGA timing and optimization problems using data analytics and computing power.
As a plugin to existing FPGA tools, InTime has built-in intelligence to provide optimized strategies for synthesis and place-and-route. It actively learns from build results to improve over time.
Yes we mean it. Get better results without modifying your design. InTime has built-in intelligence to analyze an FPGA design and determine optimized strategies for synthesis and place-and-route, delivering better results.
Let InTime decide. InTime learns from your previous build results and improves them based on device, design and tool characteristics. The more builds InTime does, the higher the likelihood of a better result becomes.
With machine learning, InTime gets smarter every time it runs.
Seed sweep must not be your first and last resort. A slight change in your RTL will render seed results unusable. Unlike a seed sweep, InTime outcomes are not random and unrelated. InTime results are derived from the analysis of previous builds. The top results from InTime can be reused and are more resilient to design changes.
InTime assimilates the computers you want into one big grid, distributing builds and collecting results across machines automatically. It works well with resource management software like LSF or SGE. You can run InTime on-premise in your company’s data center or in the cloud such as AWS. Read more about running InTime in the cloud.
InTime works with the major FPGA tools in the market, including Quartus Prime, ISE and Vivado. Every result that you see in InTime is derived from the reports of these tools. You can also export any InTime result back to the FPGA tools to verify its accuracy. Plunify is a partner of Xilinx and Intel.
The more design data you feed into InTime, the better is your analysis. Build your own internal machine learning database and use it to shorten your own design life cycles and meet deadlines.