Cadence: library characterization tool employs machine learning and cloud optimizations - Embedded.com

Cadence: library characterization tool employs machine learning and cloud optimizations

Cadence Design Systems announced the Cadence Liberate Trio Characterization Suite, a unified library characterization tool that runs both statistical and nominal characterization in parallel and provides complete validation of standard cell libraries. Employing advanced machine learning techniques, this tool uses smart interpolation to help determine the critical corners that need to be characterized. By making characterization processes thoroughly distributed and massively parallel, it has been fully optimized for running on cloud-based servers. In addition, the Liberate Trio suite offers up to 3X performance increase by running corners in parallel and natively running statistical and nominal characterization together.

The Liberate Trio Characterization Suite is part of the Cadence Cloud portfolio. The machine learning algorithms in the Liberate Trio suite help guide the designer, predicting critical corners and helping designers decide what corners need to be characterized. The tool uses smart interpolation, not just linear interpolation, to ensure accuracy.

Designers today have an increasing need for high-throughput characterization and fast, accurate re-characterization of custom corners. Cadence created a unified graphical user interface (GUI) cockpit that lets designers use a single script to efficiently launch and monitor characterization. This addresses the challenge of maintaining consistency of data across the large number of process, voltage and temperature corners. It also helps designers properly mine all the data that is collected.

Addressing the parallel nature of library characterization over hundreds of cells, characterization uses multiple CPUs to improve throughput. Now, Cadence has optimized the Liberate Trio suite to be fully cloud ready, whether employed in a public or company private cloud, and scalable to over a thousand CPUs. Characterization of a library containing over 1000 cells that would normally take weeks now can be turned around in days.

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